AUTOML and model Explainability
• 281 min read
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import seaborn as sns
%matplotlib inline
pd.set_option('display.max_rows',None)
fraud_df = pd.read_csv("C:\Books\ML Project\insurance_claims.csv",index_col='policy_number')
#fraud_df
fraud_df.head()
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 521585 | 328 | 48 | 17-10-2014 | OH | 250/500 | 1000 | 1406.91 | 0 | 466132 | MALE | ... | 2 | YES | 71610 | 6510 | 13020 | 52080 | Saab | 92x | 2004 | Y |
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 687698 | 134 | 29 | 06-09-2000 | OH | 100/300 | 2000 | 1413.14 | 5000000 | 430632 | FEMALE | ... | 3 | NO | 34650 | 7700 | 3850 | 23100 | Dodge | RAM | 2007 | N |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 367455 | 228 | 44 | 06-06-2014 | IL | 500/1000 | 1000 | 1583.91 | 6000000 | 610706 | MALE | ... | 1 | NO | 6500 | 1300 | 650 | 4550 | Accura | RSX | 2009 | N |
5 rows × 38 columns
fraud_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 1000 non-null int64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 1000 non-null float64 7 umbrella_limit 1000 non-null int64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null object 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 1000 non-null int64 31 injury_claim 1000 non-null int64 32 property_claim 1000 non-null int64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(1), int64(16), object(21) memory usage: 304.7+ KB
fraud_df.fraud_reported.value_counts()
N 753 Y 247 Name: fraud_reported, dtype: int64
fraud_df.shape
(1000, 38)
fraud_df.isnull().sum()
months_as_customer 0 age 0 policy_bind_date 0 policy_state 0 policy_csl 0 policy_deductable 0 policy_annual_premium 0 umbrella_limit 0 insured_zip 0 insured_sex 0 insured_education_level 0 insured_occupation 0 insured_hobbies 0 insured_relationship 0 capital-gains 0 capital-loss 0 incident_date 0 incident_type 0 collision_type 0 incident_severity 0 authorities_contacted 0 incident_state 0 incident_city 0 incident_location 0 incident_hour_of_the_day 0 number_of_vehicles_involved 0 property_damage 0 bodily_injuries 0 witnesses 0 police_report_available 0 total_claim_amount 0 injury_claim 0 property_claim 0 vehicle_claim 0 auto_make 0 auto_model 0 auto_year 0 fraud_reported 0 dtype: int64
fraud_df.describe()
| months_as_customer | age | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | capital-gains | capital-loss | incident_hour_of_the_day | number_of_vehicles_involved | bodily_injuries | witnesses | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_year | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1.000000e+03 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.000000 | 1000.000000 |
| mean | 203.954000 | 38.948000 | 1136.000000 | 1256.406150 | 1.101000e+06 | 501214.488000 | 25126.100000 | -26793.700000 | 11.644000 | 1.83900 | 0.992000 | 1.487000 | 52761.94000 | 7433.420000 | 7399.570000 | 37928.950000 | 2005.103000 |
| std | 115.113174 | 9.140287 | 611.864673 | 244.167395 | 2.297407e+06 | 71701.610941 | 27872.187708 | 28104.096686 | 6.951373 | 1.01888 | 0.820127 | 1.111335 | 26401.53319 | 4880.951853 | 4824.726179 | 18886.252893 | 6.015861 |
| min | 0.000000 | 19.000000 | 500.000000 | 433.330000 | -1.000000e+06 | 430104.000000 | 0.000000 | -111100.000000 | 0.000000 | 1.00000 | 0.000000 | 0.000000 | 100.00000 | 0.000000 | 0.000000 | 70.000000 | 1995.000000 |
| 25% | 115.750000 | 32.000000 | 500.000000 | 1089.607500 | 0.000000e+00 | 448404.500000 | 0.000000 | -51500.000000 | 6.000000 | 1.00000 | 0.000000 | 1.000000 | 41812.50000 | 4295.000000 | 4445.000000 | 30292.500000 | 2000.000000 |
| 50% | 199.500000 | 38.000000 | 1000.000000 | 1257.200000 | 0.000000e+00 | 466445.500000 | 0.000000 | -23250.000000 | 12.000000 | 1.00000 | 1.000000 | 1.000000 | 58055.00000 | 6775.000000 | 6750.000000 | 42100.000000 | 2005.000000 |
| 75% | 276.250000 | 44.000000 | 2000.000000 | 1415.695000 | 0.000000e+00 | 603251.000000 | 51025.000000 | 0.000000 | 17.000000 | 3.00000 | 2.000000 | 2.000000 | 70592.50000 | 11305.000000 | 10885.000000 | 50822.500000 | 2010.000000 |
| max | 479.000000 | 64.000000 | 2000.000000 | 2047.590000 | 1.000000e+07 | 620962.000000 | 100500.000000 | 0.000000 | 23.000000 | 4.00000 | 2.000000 | 3.000000 | 114920.00000 | 21450.000000 | 23670.000000 | 79560.000000 | 2015.000000 |
fraud_df.auto_year.value_counts()
1995 56 1999 55 2005 54 2011 53 2006 53 2007 52 2003 51 2010 50 2009 50 2013 49 2002 49 2015 47 1997 46 2012 46 2008 45 2014 44 2001 42 2000 42 1998 40 2004 39 1996 37 Name: auto_year, dtype: int64
fraud_df['auto_make'].value_counts()
Dodge 80 Suburu 80 Saab 80 Nissan 78 Chevrolet 76 BMW 72 Ford 72 Toyota 70 Audi 69 Volkswagen 68 Accura 68 Jeep 67 Mercedes 65 Honda 55 Name: auto_make, dtype: int64
fraud_df.auto_model.value_counts()
RAM 43 Wrangler 42 Neon 37 A3 37 MDX 36 Jetta 35 Passat 33 A5 32 Legacy 32 Pathfinder 31 Malibu 30 Camry 28 Forrestor 28 92x 28 F150 27 95 27 E400 27 93 25 Grand Cherokee 25 Escape 24 Maxima 24 Tahoe 24 X5 23 Ultima 23 Highlander 22 Silverado 22 Civic 22 Fusion 21 TL 20 ML350 20 Corolla 20 CRV 20 Impreza 20 3 Series 18 C300 18 X6 16 M5 15 Accord 13 RSX 12 Name: auto_model, dtype: int64
sns.boxplot(x=fraud_df['age'])
<AxesSubplot:xlabel='age'>
sns.pairplot(fraud_df)
<seaborn.axisgrid.PairGrid at 0x1b90580f7f0>
fraud_df.head(10)
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 521585 | 328 | 48 | 17-10-2014 | OH | 250/500 | 1000 | 1406.91 | 0 | 466132 | MALE | ... | 2 | YES | 71610 | 6510 | 13020 | 52080 | Saab | 92x | 2004 | Y |
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 687698 | 134 | 29 | 06-09-2000 | OH | 100/300 | 2000 | 1413.14 | 5000000 | 430632 | FEMALE | ... | 3 | NO | 34650 | 7700 | 3850 | 23100 | Dodge | RAM | 2007 | N |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 367455 | 228 | 44 | 06-06-2014 | IL | 500/1000 | 1000 | 1583.91 | 6000000 | 610706 | MALE | ... | 1 | NO | 6500 | 1300 | 650 | 4550 | Accura | RSX | 2009 | N |
| 104594 | 256 | 39 | 12-10-2006 | OH | 250/500 | 1000 | 1351.10 | 0 | 478456 | FEMALE | ... | 2 | NO | 64100 | 6410 | 6410 | 51280 | Saab | 95 | 2003 | Y |
| 413978 | 137 | 34 | 04-06-2000 | IN | 250/500 | 1000 | 1333.35 | 0 | 441716 | MALE | ... | 0 | ? | 78650 | 21450 | 7150 | 50050 | Nissan | Pathfinder | 2012 | N |
| 429027 | 165 | 37 | 03-02-1990 | IL | 100/300 | 1000 | 1137.03 | 0 | 603195 | MALE | ... | 2 | YES | 51590 | 9380 | 9380 | 32830 | Audi | A5 | 2015 | N |
| 485665 | 27 | 33 | 05-02-1997 | IL | 100/300 | 500 | 1442.99 | 0 | 601734 | FEMALE | ... | 1 | YES | 27700 | 2770 | 2770 | 22160 | Toyota | Camry | 2012 | N |
| 636550 | 212 | 42 | 25-07-2011 | IL | 100/300 | 500 | 1315.68 | 0 | 600983 | MALE | ... | 1 | ? | 42300 | 4700 | 4700 | 32900 | Saab | 92x | 1996 | N |
10 rows × 38 columns
sns.boxplot(x=fraud_df['months_as_customer'],y=fraud_df['fraud_reported'])
<AxesSubplot:xlabel='months_as_customer', ylabel='fraud_reported'>
sns.displot(fraud_df['months_as_customer'])
<seaborn.axisgrid.FacetGrid at 0x1b9116e1f10>
sns.distplot(fraud_df['age'])
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
<AxesSubplot:xlabel='age', ylabel='Density'>
sns.countplot(fraud_df.auto_make)
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
<AxesSubplot:xlabel='auto_make', ylabel='count'>
sns.countplot(fraud_df.fraud_reported)
C:\Users\TSK\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
<AxesSubplot:xlabel='fraud_reported', ylabel='count'>
num=[]
cat=[]
for i in fraud_df.columns:
if fraud_df[i].dtype=='object':
cat.append(i)
else:
num.append(i)
print(cat)
['policy_bind_date', 'policy_state', 'policy_csl', 'insured_sex', 'insured_education_level', 'insured_occupation', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'authorities_contacted', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'police_report_available', 'auto_make', 'auto_model', 'fraud_reported']
print(num)
['months_as_customer', 'age', 'policy_deductable', 'policy_annual_premium', 'umbrella_limit', 'insured_zip', 'capital-gains', 'capital-loss', 'incident_hour_of_the_day', 'number_of_vehicles_involved', 'bodily_injuries', 'witnesses', 'total_claim_amount', 'injury_claim', 'property_claim', 'vehicle_claim', 'auto_year']
fraud_df[num].describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| months_as_customer | 1000.0 | 2.039540e+02 | 1.151132e+02 | 0.00 | 115.7500 | 199.5 | 276.250 | 479.00 |
| age | 1000.0 | 3.894800e+01 | 9.140287e+00 | 19.00 | 32.0000 | 38.0 | 44.000 | 64.00 |
| policy_deductable | 1000.0 | 1.136000e+03 | 6.118647e+02 | 500.00 | 500.0000 | 1000.0 | 2000.000 | 2000.00 |
| policy_annual_premium | 1000.0 | 1.256406e+03 | 2.441674e+02 | 433.33 | 1089.6075 | 1257.2 | 1415.695 | 2047.59 |
| umbrella_limit | 1000.0 | 1.101000e+06 | 2.297407e+06 | -1000000.00 | 0.0000 | 0.0 | 0.000 | 10000000.00 |
| insured_zip | 1000.0 | 5.012145e+05 | 7.170161e+04 | 430104.00 | 448404.5000 | 466445.5 | 603251.000 | 620962.00 |
| capital-gains | 1000.0 | 2.512610e+04 | 2.787219e+04 | 0.00 | 0.0000 | 0.0 | 51025.000 | 100500.00 |
| capital-loss | 1000.0 | -2.679370e+04 | 2.810410e+04 | -111100.00 | -51500.0000 | -23250.0 | 0.000 | 0.00 |
| incident_hour_of_the_day | 1000.0 | 1.164400e+01 | 6.951373e+00 | 0.00 | 6.0000 | 12.0 | 17.000 | 23.00 |
| number_of_vehicles_involved | 1000.0 | 1.839000e+00 | 1.018880e+00 | 1.00 | 1.0000 | 1.0 | 3.000 | 4.00 |
| bodily_injuries | 1000.0 | 9.920000e-01 | 8.201272e-01 | 0.00 | 0.0000 | 1.0 | 2.000 | 2.00 |
| witnesses | 1000.0 | 1.487000e+00 | 1.111335e+00 | 0.00 | 1.0000 | 1.0 | 2.000 | 3.00 |
| total_claim_amount | 1000.0 | 5.276194e+04 | 2.640153e+04 | 100.00 | 41812.5000 | 58055.0 | 70592.500 | 114920.00 |
| injury_claim | 1000.0 | 7.433420e+03 | 4.880952e+03 | 0.00 | 4295.0000 | 6775.0 | 11305.000 | 21450.00 |
| property_claim | 1000.0 | 7.399570e+03 | 4.824726e+03 | 0.00 | 4445.0000 | 6750.0 | 10885.000 | 23670.00 |
| vehicle_claim | 1000.0 | 3.792895e+04 | 1.888625e+04 | 70.00 | 30292.5000 | 42100.0 | 50822.500 | 79560.00 |
| auto_year | 1000.0 | 2.005103e+03 | 6.015861e+00 | 1995.00 | 2000.0000 | 2005.0 | 2010.000 | 2015.00 |
fraud_df.replace({'umbrella_limit':-1000000.00},{'umbrella_limit':0},inplace=True)
fraud_df[num].describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| months_as_customer | 1000.0 | 2.039540e+02 | 1.151132e+02 | 0.00 | 115.7500 | 199.5 | 276.250 | 479.00 |
| age | 1000.0 | 3.894800e+01 | 9.140287e+00 | 19.00 | 32.0000 | 38.0 | 44.000 | 64.00 |
| policy_deductable | 1000.0 | 1.136000e+03 | 6.118647e+02 | 500.00 | 500.0000 | 1000.0 | 2000.000 | 2000.00 |
| policy_annual_premium | 1000.0 | 1.256406e+03 | 2.441674e+02 | 433.33 | 1089.6075 | 1257.2 | 1415.695 | 2047.59 |
| umbrella_limit | 1000.0 | 1.102000e+06 | 2.296709e+06 | 0.00 | 0.0000 | 0.0 | 0.000 | 10000000.00 |
| insured_zip | 1000.0 | 5.012145e+05 | 7.170161e+04 | 430104.00 | 448404.5000 | 466445.5 | 603251.000 | 620962.00 |
| capital-gains | 1000.0 | 2.512610e+04 | 2.787219e+04 | 0.00 | 0.0000 | 0.0 | 51025.000 | 100500.00 |
| capital-loss | 1000.0 | -2.679370e+04 | 2.810410e+04 | -111100.00 | -51500.0000 | -23250.0 | 0.000 | 0.00 |
| incident_hour_of_the_day | 1000.0 | 1.164400e+01 | 6.951373e+00 | 0.00 | 6.0000 | 12.0 | 17.000 | 23.00 |
| number_of_vehicles_involved | 1000.0 | 1.839000e+00 | 1.018880e+00 | 1.00 | 1.0000 | 1.0 | 3.000 | 4.00 |
| bodily_injuries | 1000.0 | 9.920000e-01 | 8.201272e-01 | 0.00 | 0.0000 | 1.0 | 2.000 | 2.00 |
| witnesses | 1000.0 | 1.487000e+00 | 1.111335e+00 | 0.00 | 1.0000 | 1.0 | 2.000 | 3.00 |
| total_claim_amount | 1000.0 | 5.276194e+04 | 2.640153e+04 | 100.00 | 41812.5000 | 58055.0 | 70592.500 | 114920.00 |
| injury_claim | 1000.0 | 7.433420e+03 | 4.880952e+03 | 0.00 | 4295.0000 | 6775.0 | 11305.000 | 21450.00 |
| property_claim | 1000.0 | 7.399570e+03 | 4.824726e+03 | 0.00 | 4445.0000 | 6750.0 | 10885.000 | 23670.00 |
| vehicle_claim | 1000.0 | 3.792895e+04 | 1.888625e+04 | 70.00 | 30292.5000 | 42100.0 | 50822.500 | 79560.00 |
| auto_year | 1000.0 | 2.005103e+03 | 6.015861e+00 | 1995.00 | 2000.0000 | 2005.0 | 2010.000 | 2015.00 |
fraud_df[cat].describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| policy_bind_date | 1000 | 951 | 01-01-2006 | 3 |
| policy_state | 1000 | 3 | OH | 352 |
| policy_csl | 1000 | 3 | 250/500 | 351 |
| insured_sex | 1000 | 2 | FEMALE | 537 |
| insured_education_level | 1000 | 7 | JD | 161 |
| insured_occupation | 1000 | 14 | machine-op-inspct | 93 |
| insured_hobbies | 1000 | 20 | reading | 64 |
| insured_relationship | 1000 | 6 | own-child | 183 |
| incident_date | 1000 | 60 | 02-02-2015 | 28 |
| incident_type | 1000 | 4 | Multi-vehicle Collision | 419 |
| collision_type | 1000 | 4 | Rear Collision | 292 |
| incident_severity | 1000 | 4 | Minor Damage | 354 |
| authorities_contacted | 1000 | 5 | Police | 292 |
| incident_state | 1000 | 7 | NY | 262 |
| incident_city | 1000 | 7 | Springfield | 157 |
| incident_location | 1000 | 1000 | 3340 3rd Hwy | 1 |
| property_damage | 1000 | 3 | ? | 360 |
| police_report_available | 1000 | 3 | NO | 343 |
| auto_make | 1000 | 14 | Dodge | 80 |
| auto_model | 1000 | 39 | RAM | 43 |
| fraud_reported | 1000 | 2 | N | 753 |
fraud_df.loc[fraud_df['property_damage'] == '?']
| months_as_customer | age | policy_bind_date | policy_state | policy_csl | policy_deductable | policy_annual_premium | umbrella_limit | insured_zip | insured_sex | ... | witnesses | police_report_available | total_claim_amount | injury_claim | property_claim | vehicle_claim | auto_make | auto_model | auto_year | fraud_reported | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| policy_number | |||||||||||||||||||||
| 342868 | 228 | 42 | 27-06-2006 | IN | 250/500 | 2000 | 1197.22 | 5000000 | 468176 | MALE | ... | 0 | ? | 5070 | 780 | 780 | 3510 | Mercedes | E400 | 2007 | Y |
| 227811 | 256 | 41 | 25-05-1990 | IL | 250/500 | 2000 | 1415.74 | 6000000 | 608117 | FEMALE | ... | 2 | NO | 63400 | 6340 | 6340 | 50720 | Chevrolet | Tahoe | 2014 | Y |
| 413978 | 137 | 34 | 04-06-2000 | IN | 250/500 | 1000 | 1333.35 | 0 | 441716 | MALE | ... | 0 | ? | 78650 | 21450 | 7150 | 50050 | Nissan | Pathfinder | 2012 | N |
| 429027 | 165 | 37 | 03-02-1990 | IL | 100/300 | 1000 | 1137.03 | 0 | 603195 | MALE | ... | 2 | YES | 51590 | 9380 | 9380 | 32830 | Audi | A5 | 2015 | N |
| 558938 | 70 | 26 | 08-06-2005 | OH | 500/1000 | 1000 | 1199.44 | 5000000 | 619884 | MALE | ... | 2 | YES | 52110 | 5790 | 5790 | 40530 | Nissan | Maxima | 2012 | N |
| 143972 | 196 | 39 | 02-08-1992 | IN | 500/1000 | 2000 | 1475.73 | 0 | 477670 | FEMALE | ... | 0 | NO | 60400 | 6040 | 6040 | 48320 | Nissan | Pathfinder | 2014 | N |
| 431876 | 217 | 41 | 27-11-2005 | IL | 500/1000 | 2000 | 875.15 | 0 | 442479 | FEMALE | ... | 2 | ? | 37840 | 0 | 4730 | 33110 | Accura | RSX | 1996 | N |
| 115399 | 413 | 55 | 08-02-1991 | IN | 100/300 | 2000 | 1268.79 | 0 | 453148 | MALE | ... | 2 | ? | 98160 | 8180 | 16360 | 73620 | Dodge | RAM | 2011 | Y |
| 200152 | 62 | 28 | 09-03-2003 | IL | 100/300 | 1000 | 988.45 | 0 | 430141 | FEMALE | ... | 1 | YES | 60200 | 6020 | 6020 | 48160 | Suburu | Forrestor | 2004 | Y |
| 485664 | 431 | 54 | 25-11-2002 | IN | 500/1000 | 2000 | 1155.55 | 0 | 615901 | FEMALE | ... | 0 | ? | 62300 | 12460 | 6230 | 43610 | Jeep | Wrangler | 2007 | N |
| 982871 | 199 | 37 | 27-07-1997 | IN | 250/500 | 500 | 1262.08 | 0 | 474615 | MALE | ... | 3 | NO | 60170 | 10940 | 10940 | 38290 | Nissan | Pathfinder | 2011 | Y |
| 616337 | 116 | 34 | 30-08-2012 | IN | 250/500 | 500 | 1737.66 | 0 | 470577 | MALE | ... | 1 | ? | 97080 | 16180 | 16180 | 64720 | BMW | X5 | 2001 | Y |
| 866931 | 175 | 34 | 07-01-2008 | IN | 500/1000 | 1000 | 1123.87 | 8000000 | 446326 | FEMALE | ... | 0 | YES | 7290 | 810 | 810 | 5670 | Volkswagen | Passat | 1995 | N |
| 691189 | 430 | 59 | 10-01-2004 | OH | 250/500 | 2000 | 1326.62 | 7000000 | 477310 | MALE | ... | 3 | YES | 81800 | 16360 | 8180 | 57260 | Nissan | Pathfinder | 1998 | N |
| 537546 | 91 | 27 | 20-08-1994 | IL | 100/300 | 2000 | 1073.83 | 0 | 609930 | FEMALE | ... | 2 | ? | 7260 | 1320 | 660 | 5280 | BMW | M5 | 2008 | N |
| 394975 | 217 | 39 | 02-06-2002 | IN | 100/300 | 1000 | 1530.52 | 0 | 603993 | MALE | ... | 1 | YES | 4300 | 430 | 430 | 3440 | Toyota | Corolla | 2000 | N |
| 524836 | 439 | 56 | 20-11-2008 | IN | 250/500 | 500 | 1082.49 | 0 | 606714 | FEMALE | ... | 3 | ? | 56430 | 0 | 6270 | 50160 | Honda | CRV | 2014 | N |
| 486676 | 271 | 42 | 15-08-2011 | OH | 100/300 | 500 | 1105.49 | 0 | 463181 | FEMALE | ... | 3 | ? | 68310 | 12420 | 6210 | 49680 | Audi | A3 | 2003 | Y |
| 279422 | 227 | 38 | 27-10-2013 | OH | 500/1000 | 500 | 976.67 | 0 | 471600 | FEMALE | ... | 2 | ? | 72200 | 14440 | 7220 | 50540 | BMW | M5 | 2013 | Y |
| 645258 | 244 | 40 | 04-07-1997 | OH | 500/1000 | 2000 | 1267.81 | 5000000 | 603123 | FEMALE | ... | 1 | ? | 6600 | 660 | 1320 | 4620 | Accura | TL | 2005 | N |
| 498140 | 284 | 48 | 15-05-1997 | IN | 500/1000 | 2000 | 769.95 | 0 | 605486 | MALE | ... | 3 | NO | 60940 | 5540 | 11080 | 44320 | Audi | A3 | 2013 | Y |
| 614763 | 134 | 32 | 02-01-1991 | IL | 500/1000 | 500 | 1612.43 | 0 | 456762 | FEMALE | ... | 1 | YES | 64240 | 11680 | 11680 | 40880 | BMW | 3 Series | 2015 | N |
| 740019 | 295 | 48 | 17-06-2009 | OH | 250/500 | 1000 | 1148.73 | 0 | 439787 | FEMALE | ... | 2 | YES | 5400 | 900 | 900 | 3600 | Saab | 95 | 1999 | N |
| 389238 | 279 | 41 | 06-06-2001 | IL | 250/500 | 500 | 1497.35 | 0 | 460742 | FEMALE | ... | 3 | NO | 28800 | 0 | 3600 | 25200 | Ford | Fusion | 2013 | N |
| 632627 | 464 | 61 | 07-10-1990 | OH | 500/1000 | 1000 | 1125.37 | 0 | 604450 | FEMALE | ... | 2 | YES | 79800 | 6650 | 19950 | 53200 | Saab | 95 | 2000 | Y |
| 163161 | 298 | 47 | 11-11-1998 | IL | 500/1000 | 2000 | 1338.50 | 0 | 618929 | FEMALE | ... | 0 | ? | 70590 | 10860 | 10860 | 48870 | Saab | 93 | 2000 | Y |
| 776860 | 261 | 42 | 11-01-2009 | OH | 250/500 | 500 | 1337.56 | 0 | 605141 | FEMALE | ... | 2 | YES | 74700 | 7470 | 14940 | 52290 | Dodge | RAM | 2010 | N |
| 395269 | 210 | 41 | 02-11-2012 | IL | 500/1000 | 500 | 1222.75 | 0 | 432781 | MALE | ... | 0 | NO | 81070 | 14740 | 14740 | 51590 | BMW | X5 | 2001 | N |
| 981123 | 168 | 32 | 04-05-2000 | IN | 100/300 | 1000 | 1059.52 | 0 | 452748 | MALE | ... | 1 | ? | 57720 | 14430 | 9620 | 33670 | Saab | 93 | 2007 | N |
| 330591 | 225 | 41 | 05-08-1993 | OH | 500/1000 | 2000 | 1103.58 | 0 | 475292 | MALE | ... | 2 | NO | 70400 | 6400 | 12800 | 51200 | Toyota | Highlander | 2011 | Y |
| 531640 | 245 | 39 | 21-04-2001 | OH | 250/500 | 500 | 964.79 | 8000000 | 460675 | FEMALE | ... | 1 | ? | 72820 | 13240 | 6620 | 52960 | BMW | 3 Series | 2010 | N |
| 155724 | 203 | 38 | 20-02-1998 | IL | 250/500 | 500 | 1394.43 | 0 | 606352 | FEMALE | ... | 1 | YES | 55440 | 0 | 6160 | 49280 | Nissan | Maxima | 1999 | Y |
| 428230 | 165 | 32 | 04-06-2012 | IN | 500/1000 | 500 | 1399.26 | 0 | 611586 | FEMALE | ... | 0 | ? | 3960 | 330 | 660 | 2970 | BMW | M5 | 1998 | N |
| 469874 | 81 | 28 | 17-09-2011 | IL | 250/500 | 1000 | 1139.00 | 6000000 | 617858 | FEMALE | ... | 2 | ? | 44730 | 4970 | 4970 | 34790 | Saab | 92x | 2000 | Y |
| 620215 | 194 | 39 | 27-07-2005 | IN | 250/500 | 500 | 823.17 | 0 | 455689 | MALE | ... | 2 | NO | 61500 | 6150 | 12300 | 43050 | Dodge | RAM | 2012 | N |
| 618659 | 112 | 27 | 18-10-2005 | OH | 100/300 | 500 | 965.13 | 0 | 450341 | FEMALE | ... | 1 | ? | 51000 | 8500 | 8500 | 34000 | Nissan | Pathfinder | 2013 | N |
| 649082 | 24 | 33 | 19-01-1996 | IL | 500/1000 | 1000 | 1922.84 | 0 | 431277 | FEMALE | ... | 1 | NO | 46800 | 4680 | 9360 | 32760 | Jeep | Wrangler | 2002 | N |
| 437573 | 93 | 32 | 29-09-2005 | OH | 250/500 | 2000 | 1624.82 | 0 | 454656 | MALE | ... | 1 | ? | 78120 | 17360 | 8680 | 52080 | BMW | 3 Series | 2006 | N |
| 932502 | 200 | 40 | 11-05-2010 | IL | 100/300 | 1000 | 1439.34 | 0 | 444822 | FEMALE | ... | 0 | NO | 3690 | 410 | 410 | 2870 | Ford | Escape | 2015 | N |
| 935277 | 325 | 46 | 09-07-2013 | IL | 500/1000 | 500 | 1348.83 | 0 | 474360 | FEMALE | ... | 2 | YES | 76120 | 13840 | 6920 | 55360 | Toyota | Camry | 1999 | N |
| 456604 | 287 | 41 | 29-03-2004 | IL | 500/1000 | 2000 | 968.74 | 0 | 477519 | MALE | ... | 3 | ? | 5170 | 470 | 940 | 3760 | Suburu | Forrestor | 2001 | N |
| 139872 | 122 | 34 | 01-06-2006 | IN | 250/500 | 1000 | 1220.71 | 0 | 603639 | MALE | ... | 1 | YES | 8190 | 1890 | 1260 | 5040 | Chevrolet | Silverado | 2013 | N |
| 354105 | 22 | 29 | 08-06-1994 | IN | 250/500 | 2000 | 1238.62 | 6000000 | 463993 | MALE | ... | 3 | ? | 70800 | 7080 | 14160 | 49560 | Accura | MDX | 2012 | Y |
| 165485 | 106 | 31 | 12-02-1998 | IL | 500/1000 | 2000 | 1320.75 | 0 | 441491 | FEMALE | ... | 3 | YES | 45630 | 5070 | 5070 | 35490 | Accura | MDX | 2011 | N |
| 795686 | 214 | 41 | 24-10-2004 | IL | 500/1000 | 500 | 1398.51 | 4000000 | 472214 | MALE | ... | 0 | NO | 64000 | 12800 | 6400 | 44800 | BMW | X6 | 1996 | Y |
| 395983 | 209 | 38 | 08-11-2009 | OH | 100/300 | 500 | 1355.08 | 0 | 614945 | FEMALE | ... | 1 | ? | 47300 | 4730 | 4730 | 37840 | Nissan | Pathfinder | 2014 | N |
| 217938 | 193 | 41 | 16-07-1995 | OH | 250/500 | 500 | 847.03 | 0 | 438555 | FEMALE | ... | 0 | YES | 112320 | 17280 | 17280 | 77760 | Suburu | Impreza | 2011 | Y |
| 203914 | 134 | 32 | 09-06-2001 | OH | 100/300 | 1000 | 1000.06 | 0 | 440961 | FEMALE | ... | 0 | ? | 82720 | 7520 | 15040 | 60160 | Audi | A3 | 2014 | N |
| 565157 | 288 | 45 | 06-10-2002 | IL | 100/300 | 1000 | 1046.71 | 0 | 616714 | MALE | ... | 1 | ? | 48060 | 10680 | 5340 | 32040 | Mercedes | C300 | 2009 | N |
| 781181 | 461 | 61 | 27-06-2005 | OH | 100/300 | 2000 | 1402.75 | 0 | 449557 | MALE | ... | 1 | YES | 72100 | 7210 | 14420 | 50470 | Jeep | Wrangler | 2006 | N |
| 299796 | 428 | 59 | 29-09-1999 | IN | 250/500 | 500 | 1344.36 | 7000000 | 473329 | FEMALE | ... | 3 | NO | 6500 | 1300 | 650 | 4550 | Saab | 92x | 2013 | N |
| 589749 | 45 | 38 | 14-05-2006 | IN | 100/300 | 1000 | 1197.71 | 0 | 470117 | MALE | ... | 0 | ? | 78240 | 13040 | 13040 | 52160 | Suburu | Legacy | 2013 | N |
| 854021 | 136 | 29 | 29-04-2010 | OH | 100/300 | 500 | 1203.24 | 0 | 600702 | FEMALE | ... | 0 | ? | 6200 | 1240 | 620 | 4340 | Honda | Accord | 1999 | N |
| 139484 | 278 | 48 | 24-07-1999 | IN | 500/1000 | 2000 | 1142.62 | 7000000 | 475588 | FEMALE | ... | 0 | ? | 76050 | 11700 | 11700 | 52650 | Chevrolet | Silverado | 1997 | N |
| 335780 | 14 | 28 | 22-07-2002 | OH | 250/500 | 2000 | 1587.96 | 0 | 601617 | FEMALE | ... | 1 | YES | 71280 | 12960 | 12960 | 45360 | Audi | A5 | 2012 | Y |
| 140880 | 276 | 46 | 29-03-2005 | IL | 250/500 | 500 | 1448.84 | 0 | 430987 | FEMALE | ... | 1 | NO | 5940 | 660 | 660 | 4620 | Toyota | Highlander | 2015 | N |
| 288580 | 119 | 28 | 22-11-2012 | OH | 250/500 | 2000 | 1079.92 | 0 | 430886 | MALE | ... | 1 | YES | 53600 | 6700 | 6700 | 40200 | Volkswagen | Jetta | 2007 | Y |
| 425973 | 8 | 31 | 11-02-2003 | IN | 250/500 | 500 | 1229.16 | 4000000 | 604804 | FEMALE | ... | 2 | YES | 48100 | 11100 | 7400 | 29600 | Volkswagen | Jetta | 2014 | N |
| 648509 | 324 | 46 | 06-03-2010 | IN | 100/300 | 2000 | 897.89 | 6000000 | 618862 | MALE | ... | 0 | YES | 79600 | 15920 | 15920 | 47760 | Jeep | Wrangler | 2011 | N |
| 651861 | 235 | 39 | 07-01-2011 | IL | 100/300 | 500 | 1046.58 | 4000000 | 434982 | MALE | ... | 1 | NO | 4950 | 450 | 900 | 3600 | Chevrolet | Silverado | 2010 | N |
| 125324 | 53 | 36 | 13-09-2003 | OH | 500/1000 | 2000 | 1712.68 | 0 | 614233 | MALE | ... | 0 | YES | 51100 | 10220 | 5110 | 35770 | Audi | A3 | 2006 | N |
| 391652 | 86 | 26 | 12-10-1998 | OH | 100/300 | 500 | 1382.88 | 7000000 | 434923 | MALE | ... | 1 | YES | 81840 | 14880 | 7440 | 59520 | Chevrolet | Malibu | 2011 | Y |
| 922167 | 296 | 46 | 23-02-1993 | OH | 100/300 | 1000 | 1141.35 | 7000000 | 476456 | MALE | ... | 2 | NO | 54900 | 5490 | 5490 | 43920 | Mercedes | C300 | 2013 | N |
| 226330 | 177 | 34 | 23-01-2013 | IL | 100/300 | 2000 | 1057.77 | 0 | 477382 | FEMALE | ... | 1 | NO | 18000 | 2250 | 2250 | 13500 | Audi | A3 | 2009 | N |
| 524230 | 81 | 25 | 23-02-2014 | IN | 100/300 | 500 | 920.30 | 5000000 | 461958 | FEMALE | ... | 0 | ? | 73920 | 13440 | 6720 | 53760 | Honda | Civic | 2003 | Y |
| 868283 | 252 | 39 | 06-02-2006 | IN | 250/500 | 1000 | 1086.21 | 0 | 455340 | MALE | ... | 2 | ? | 50490 | 5610 | 5610 | 39270 | Nissan | Maxima | 2001 | N |
| 918777 | 73 | 26 | 04-04-2003 | IL | 250/500 | 2000 | 1191.19 | 4000000 | 468813 | MALE | ... | 1 | YES | 40160 | 5020 | 0 | 35140 | Chevrolet | Tahoe | 2003 | N |
| 602410 | 196 | 36 | 16-01-1996 | IN | 250/500 | 2000 | 1463.07 | 0 | 615611 | MALE | ... | 1 | NO | 5300 | 530 | 530 | 4240 | Jeep | Grand Cherokee | 2001 | Y |
| 171254 | 285 | 43 | 07-11-1994 | OH | 100/300 | 2000 | 1512.58 | 0 | 452496 | FEMALE | ... | 1 | ? | 2520 | 280 | 280 | 1960 | BMW | 3 Series | 1997 | N |
| 505969 | 342 | 49 | 07-04-1998 | OH | 250/500 | 500 | 1722.95 | 0 | 472634 | MALE | ... | 0 | YES | 76700 | 7670 | 7670 | 61360 | Suburu | Legacy | 2006 | N |
| 547744 | 128 | 32 | 08-07-2001 | OH | 100/300 | 2000 | 768.91 | 0 | 443522 | FEMALE | ... | 0 | NO | 59800 | 5980 | 5980 | 47840 | Ford | F150 | 1999 | Y |
| 598124 | 124 | 29 | 20-09-1993 | OH | 500/1000 | 500 | 1301.72 | 0 | 441726 | MALE | ... | 3 | YES | 72000 | 7200 | 7200 | 57600 | Audi | A3 | 2005 | N |
| 218684 | 210 | 37 | 05-08-2006 | IN | 500/1000 | 2000 | 1048.46 | 0 | 466676 | MALE | ... | 2 | ? | 7080 | 1180 | 590 | 5310 | Dodge | RAM | 1999 | N |
| 743163 | 11 | 40 | 09-04-2001 | OH | 500/1000 | 2000 | 1217.69 | 0 | 440106 | FEMALE | ... | 0 | ? | 53460 | 9720 | 9720 | 34020 | Dodge | Neon | 2004 | Y |
| 509928 | 272 | 43 | 25-07-1995 | OH | 100/300 | 1000 | 1279.13 | 0 | 615226 | MALE | ... | 2 | YES | 81070 | 7370 | 14740 | 58960 | Audi | A3 | 2006 | Y |
| 970607 | 38 | 28 | 28-03-1995 | OH | 250/500 | 1000 | 1019.44 | 0 | 437387 | MALE | ... | 1 | NO | 7200 | 1440 | 720 | 5040 | BMW | X5 | 2004 | N |
| 174701 | 328 | 46 | 19-06-1996 | IL | 500/1000 | 500 | 1314.60 | 0 | 458139 | FEMALE | ... | 3 | ? | 70290 | 12780 | 6390 | 51120 | Saab | 92x | 1998 | Y |
| 114839 | 362 | 50 | 01-01-2006 | IL | 250/500 | 500 | 1198.34 | 4000000 | 619735 | MALE | ... | 1 | NO | 57060 | 6340 | 6340 | 44380 | Mercedes | E400 | 1995 | N |
| 872734 | 241 | 38 | 19-05-1990 | IN | 100/300 | 2000 | 1003.23 | 0 | 470485 | FEMALE | ... | 3 | YES | 77880 | 12980 | 12980 | 51920 | Accura | MDX | 2008 | N |
| 629663 | 343 | 52 | 21-01-2002 | IL | 500/1000 | 1000 | 1053.02 | 0 | 602402 | FEMALE | ... | 2 | NO | 47630 | 12990 | 4330 | 30310 | Toyota | Corolla | 2005 | Y |
| 241562 | 154 | 37 | 28-01-2010 | IL | 250/500 | 1000 | 2047.59 | 0 | 439269 | FEMALE | ... | 3 | NO | 79530 | 14460 | 7230 | 57840 | Accura | MDX | 2000 | N |
| 511621 | 235 | 42 | 22-09-1990 | IN | 250/500 | 500 | 1072.62 | 0 | 444913 | FEMALE | ... | 1 | YES | 69100 | 6910 | 6910 | 55280 | Toyota | Highlander | 2006 | N |
| 476923 | 172 | 35 | 19-09-2004 | IL | 100/300 | 2000 | 1219.04 | 0 | 456602 | MALE | ... | 0 | NO | 79750 | 14500 | 14500 | 50750 | Nissan | Pathfinder | 1999 | N |
| 735822 | 27 | 28 | 28-08-1995 | IN | 100/300 | 2000 | 1371.78 | 0 | 451560 | MALE | ... | 2 | YES | 53600 | 5360 | 10720 | 37520 | Audi | A5 | 2015 | Y |
| 280709 | 272 | 41 | 06-05-1991 | OH | 500/1000 | 2000 | 1608.34 | 0 | 466718 | FEMALE | ... | 1 | ? | 71060 | 6460 | 12920 | 51680 | Saab | 92x | 2010 | N |
| 547802 | 249 | 43 | 03-09-2013 | IL | 250/500 | 1000 | 1518.46 | 0 | 606238 | FEMALE | ... | 0 | YES | 53500 | 5350 | 5350 | 42800 | Saab | 92x | 2015 | N |
| 390381 | 190 | 40 | 27-01-2007 | OH | 500/1000 | 2000 | 965.21 | 0 | 610354 | FEMALE | ... | 1 | YES | 6300 | 630 | 630 | 5040 | Nissan | Ultima | 2001 | N |
| 629918 | 174 | 36 | 14-10-2005 | IL | 100/300 | 2000 | 1278.75 | 0 | 461328 | FEMALE | ... | 2 | NO | 65400 | 10900 | 10900 | 43600 | Dodge | RAM | 2012 | N |
| 187775 | 269 | 44 | 21-12-2002 | OH | 100/300 | 500 | 1297.75 | 4000000 | 451280 | FEMALE | ... | 1 | ? | 98670 | 15180 | 15180 | 68310 | Chevrolet | Tahoe | 2010 | Y |
| 326322 | 101 | 27 | 10-02-2007 | IL | 250/500 | 1000 | 433.33 | 0 | 603269 | MALE | ... | 3 | NO | 5900 | 1180 | 590 | 4130 | Mercedes | E400 | 2009 | N |
| 146138 | 94 | 30 | 01-03-2002 | IN | 250/500 | 2000 | 1025.54 | 0 | 442632 | FEMALE | ... | 3 | YES | 64100 | 6410 | 6410 | 51280 | Chevrolet | Malibu | 2001 | N |
| 212674 | 440 | 61 | 01-09-1992 | OH | 250/500 | 500 | 1050.76 | 0 | 467942 | MALE | ... | 3 | NO | 53640 | 5960 | 5960 | 41720 | Nissan | Maxima | 2004 | Y |
| 843227 | 134 | 30 | 28-09-2007 | OH | 250/500 | 2000 | 1443.32 | 0 | 613287 | FEMALE | ... | 2 | YES | 47790 | 5310 | 5310 | 37170 | Suburu | Impreza | 1995 | Y |
| 283925 | 122 | 29 | 21-11-1991 | OH | 250/500 | 1000 | 1629.94 | 0 | 620104 | FEMALE | ... | 1 | NO | 3850 | 350 | 350 | 3150 | Dodge | Neon | 2014 | N |
| 475588 | 156 | 31 | 21-09-1996 | IL | 100/300 | 2000 | 1134.08 | 0 | 446895 | MALE | ... | 0 | ? | 59000 | 5900 | 5900 | 47200 | Ford | Fusion | 2013 | Y |
| 226725 | 244 | 40 | 11-08-1999 | IN | 500/1000 | 2000 | 1304.67 | 7000000 | 605408 | MALE | ... | 1 | ? | 61490 | 5590 | 11180 | 44720 | Dodge | RAM | 2001 | N |
| 889883 | 246 | 45 | 03-02-1999 | IL | 250/500 | 1000 | 1665.45 | 0 | 445853 | MALE | ... | 2 | ? | 53280 | 11840 | 5920 | 35520 | Toyota | Camry | 2006 | Y |
| 577810 | 369 | 55 | 15-04-2013 | OH | 250/500 | 2000 | 1589.54 | 0 | 444734 | MALE | ... | 0 | YES | 85300 | 17060 | 8530 | 59710 | Toyota | Highlander | 2003 | N |
| 727792 | 107 | 26 | 19-05-2014 | OH | 100/300 | 500 | 974.59 | 0 | 466838 | FEMALE | ... | 0 | NO | 59700 | 11940 | 11940 | 35820 | Nissan | Ultima | 2002 | N |
| 367595 | 243 | 43 | 03-02-2006 | IN | 500/1000 | 500 | 1307.74 | 0 | 466137 | FEMALE | ... | 1 | NO | 37530 | 4170 | 4170 | 29190 | Jeep | Wrangler | 2008 | N |
| 512813 | 108 | 33 | 27-01-1990 | IL | 100/300 | 2000 | 694.45 | 0 | 450703 | FEMALE | ... | 1 | YES | 64350 | 5850 | 11700 | 46800 | Nissan | Pathfinder | 2011 | Y |
| 987905 | 436 | 58 | 30-04-2002 | OH | 250/500 | 2000 | 1407.01 | 5000000 | 475705 | MALE | ... | 2 | ? | 59900 | 11980 | 5990 | 41930 | BMW | X5 | 1997 | Y |
| 967756 | 189 | 36 | 28-04-2007 | OH | 250/500 | 2000 | 1388.58 | 0 | 459122 | FEMALE | ... | 3 | YES | 79560 | 13260 | 13260 | 53040 | Ford | Escape | 2009 | N |
| 786957 | 219 | 40 | 29-10-2006 | OH | 100/300 | 500 | 1134.91 | 0 | 452735 | FEMALE | ... | 0 | NO | 6400 | 640 | 640 | 5120 | Toyota | Camry | 1997 | N |
| 444422 | 59 | 40 | 28-09-2011 | IL | 250/500 | 2000 | 782.23 | 0 | 449221 | MALE | ... | 2 | NO | 51570 | 5730 | 11460 | 34380 | BMW | X5 | 2010 | N |
| 689500 | 39 | 31 | 28-01-2003 | IL | 250/500 | 2000 | 1366.90 | 0 | 459322 | FEMALE | ... | 0 | NO | 52700 | 10540 | 10540 | 31620 | BMW | X6 | 2014 | Y |
| 384618 | 156 | 37 | 09-02-1993 | IN | 250/500 | 500 | 1090.65 | 0 | 608331 | MALE | ... | 2 | YES | 53400 | 5340 | 5340 | 42720 | Honda | Civic | 2010 | Y |
| 756459 | 123 | 31 | 05-08-2005 | IN | 250/500 | 500 | 1326.00 | 0 | 438546 | FEMALE | ... | 2 | NO | 72120 | 12020 | 12020 | 48080 | Mercedes | ML350 | 2009 | N |
| 419954 | 247 | 39 | 07-12-1993 | IL | 100/300 | 500 | 806.31 | 0 | 602177 | FEMALE | ... | 3 | ? | 3300 | 600 | 0 | 2700 | Dodge | RAM | 2003 | N |
| 426708 | 267 | 40 | 09-10-2009 | IL | 250/500 | 500 | 1155.53 | 5000000 | 465158 | MALE | ... | 2 | NO | 5670 | 1260 | 630 | 3780 | Ford | F150 | 1997 | N |
| 771236 | 175 | 34 | 29-05-1995 | OH | 100/300 | 500 | 915.29 | 0 | 607893 | FEMALE | ... | 1 | YES | 36720 | 4080 | 4080 | 28560 | Honda | Accord | 2009 | Y |
| 235869 | 111 | 29 | 22-01-2011 | IL | 250/500 | 500 | 1239.55 | 2000000 | 464736 | FEMALE | ... | 1 | YES | 52800 | 4800 | 9600 | 38400 | Mercedes | ML350 | 1996 | Y |
| 371635 | 156 | 37 | 13-10-1991 | OH | 500/1000 | 1000 | 1086.48 | 6000000 | 444903 | MALE | ... | 1 | YES | 77440 | 14080 | 7040 | 56320 | Suburu | Legacy | 1999 | N |
| 582973 | 10 | 26 | 11-06-2008 | IN | 100/300 | 2000 | 765.64 | 0 | 466191 | MALE | ... | 3 | ? | 31350 | 2850 | 5700 | 22800 | Honda | Accord | 2001 | Y |
| 278091 | 158 | 33 | 04-12-2013 | OH | 100/300 | 2000 | 1327.41 | 0 | 440930 | FEMALE | ... | 0 | ? | 35000 | 3500 | 7000 | 24500 | Suburu | Legacy | 2012 | N |
| 153154 | 436 | 59 | 21-08-2010 | OH | 500/1000 | 1000 | 1338.55 | 0 | 430380 | MALE | ... | 2 | NO | 68000 | 13600 | 6800 | 47600 | Toyota | Corolla | 2014 | N |
| 162004 | 1 | 33 | 19-09-1995 | IL | 250/500 | 500 | 903.32 | 0 | 451184 | FEMALE | ... | 0 | ? | 31700 | 6340 | 3170 | 22190 | Toyota | Highlander | 2006 | N |
| 576723 | 266 | 44 | 07-12-1999 | IL | 250/500 | 500 | 1611.83 | 0 | 460176 | MALE | ... | 2 | YES | 32800 | 3280 | 3280 | 26240 | BMW | 3 Series | 2012 | N |
| 414913 | 257 | 40 | 17-07-2012 | IN | 250/500 | 500 | 1379.93 | 0 | 608228 | MALE | ... | 2 | YES | 51810 | 9420 | 4710 | 37680 | Audi | A3 | 2002 | Y |
| 818413 | 129 | 28 | 23-02-1990 | OH | 500/1000 | 1000 | 1377.94 | 0 | 442540 | MALE | ... | 3 | ? | 44640 | 9920 | 4960 | 29760 | Toyota | Camry | 2005 | N |
| 487356 | 283 | 46 | 30-08-2000 | IL | 500/1000 | 2000 | 1313.33 | 0 | 455332 | FEMALE | ... | 3 | NO | 77660 | 7060 | 14120 | 56480 | Nissan | Maxima | 2004 | Y |
| 865839 | 101 | 26 | 02-08-1991 | IL | 500/1000 | 1000 | 1371.88 | 0 | 462420 | FEMALE | ... | 2 | ? | 3190 | 580 | 580 | 2030 | Suburu | Legacy | 1995 | N |
| 406567 | 96 | 30 | 25-09-2001 | OH | 100/300 | 500 | 1399.27 | 6000000 | 448913 | MALE | ... | 0 | YES | 53440 | 0 | 6680 | 46760 | Ford | Escape | 2004 | N |
| 563878 | 120 | 30 | 16-07-2002 | IN | 250/500 | 500 | 956.69 | 0 | 438237 | FEMALE | ... | 1 | YES | 87100 | 8710 | 8710 | 69680 | Saab | 92x | 2000 | N |
| 620855 | 212 | 35 | 29-04-1990 | IN | 500/1000 | 2000 | 1123.89 | 0 | 468313 | MALE | ... | 3 | ? | 50380 | 4580 | 4580 | 41220 | Suburu | Forrestor | 1996 | N |
| 445973 | 66 | 26 | 13-11-1998 | IL | 250/500 | 1000 | 988.29 | 0 | 476502 | MALE | ... | 2 | YES | 57860 | 0 | 10520 | 47340 | Suburu | Impreza | 2008 | Y |
| 156694 | 334 | 47 | 24-05-2001 | IL | 500/1000 | 500 | 1238.89 | 0 | 600561 | MALE | ... | 3 | NO | 6240 | 960 | 960 | 4320 | Ford | Fusion | 2011 | N |
| 421940 | 216 | 38 | 03-06-2014 | IN | 100/300 | 1000 | 1384.64 | 5000000 | 600754 | FEMALE | ... | 3 | NO | 66600 | 16650 | 11100 | 38850 | Jeep | Grand Cherokee | 2012 | Y |
| 553565 | 257 | 43 | 18-02-1999 | IN | 500/1000 | 2000 | 929.70 | 6000000 | 618632 | FEMALE | ... | 2 | YES | 63240 | 10540 | 5270 | 47430 | Mercedes | E400 | 2005 | N |
| 331595 | 230 | 39 | 29-11-1999 | IL | 100/300 | 1000 | 904.70 | 7000000 | 454530 | FEMALE | ... | 3 | NO | 74200 | 14840 | 14840 | 44520 | Accura | TL | 2002 | Y |
| 701521 | 270 | 44 | 05-07-2003 | IL | 500/1000 | 2000 | 1030.95 | 0 | 435985 | FEMALE | ... | 0 | NO | 35900 | 7180 | 3590 | 25130 | Audi | A3 | 2007 | Y |
| 958785 | 475 | 57 | 18-02-1995 | OH | 100/300 | 500 | 1216.56 | 0 | 436522 | MALE | ... | 2 | NO | 78000 | 6500 | 13000 | 58500 | Suburu | Forrestor | 2000 | N |
| 563837 | 177 | 33 | 30-12-2002 | IL | 100/300 | 1000 | 1609.67 | 0 | 470128 | MALE | ... | 3 | ? | 82800 | 20700 | 13800 | 48300 | Jeep | Grand Cherokee | 2004 | Y |
| 386690 | 162 | 31 | 21-02-2006 | IN | 100/300 | 1000 | 1050.24 | 0 | 456789 | FEMALE | ... | 0 | NO | 3600 | 360 | 720 | 2520 | BMW | X5 | 2013 | Y |
| 979285 | 154 | 36 | 17-12-2003 | IL | 250/500 | 2000 | 1313.51 | 7000000 | 600904 | FEMALE | ... | 0 | ? | 2800 | 280 | 280 | 2240 | Volkswagen | Passat | 2015 | N |
| 216738 | 10 | 19 | 05-08-2014 | IN | 250/500 | 1000 | 1185.78 | 0 | 478837 | FEMALE | ... | 2 | ? | 48950 | 4450 | 8900 | 35600 | Accura | TL | 2011 | Y |
| 954191 | 273 | 41 | 17-02-2010 | OH | 500/1000 | 1000 | 1403.90 | 0 | 449793 | FEMALE | ... | 2 | YES | 44110 | 4010 | 8020 | 32080 | Honda | Accord | 2015 | N |
| 457244 | 419 | 53 | 28-01-1998 | IL | 500/1000 | 2000 | 736.07 | 6000000 | 445339 | MALE | ... | 0 | YES | 62280 | 5190 | 10380 | 46710 | Suburu | Forrestor | 2012 | N |
| 206667 | 315 | 44 | 05-05-1993 | IL | 250/500 | 1000 | 974.16 | 6000000 | 438328 | FEMALE | ... | 0 | YES | 26730 | 4860 | 4860 | 17010 | Volkswagen | Jetta | 2006 | N |
| 745200 | 72 | 29 | 06-08-1994 | OH | 500/1000 | 500 | 973.80 | 0 | 479913 | FEMALE | ... | 0 | NO | 66200 | 6620 | 6620 | 52960 | Dodge | Neon | 2013 | N |
| 412703 | 32 | 26 | 14-11-2014 | OH | 100/300 | 2000 | 1260.32 | 6000000 | 460760 | MALE | ... | 2 | ? | 45500 | 9100 | 4550 | 31850 | Toyota | Corolla | 2009 | N |
| 736771 | 230 | 41 | 14-12-1991 | IN | 100/300 | 1000 | 1464.03 | 0 | 444797 | MALE | ... | 0 | ? | 53040 | 4420 | 4420 | 44200 | Audi | A3 | 2006 | N |
| 347984 | 157 | 32 | 21-10-2009 | OH | 100/300 | 2000 | 617.11 | 0 | 436711 | MALE | ... | 2 | NO | 50800 | 10160 | 5080 | 35560 | Mercedes | E400 | 2013 | Y |
| 626074 | 265 | 41 | 29-09-1997 | IN | 250/500 | 2000 | 1724.46 | 6000000 | 432491 | FEMALE | ... | 3 | ? | 44200 | 4420 | 4420 | 35360 | Audi | A5 | 2014 | N |
| 999435 | 113 | 29 | 01-01-2008 | OH | 250/500 | 2000 | 1091.73 | 0 | 601213 | MALE | ... | 2 | YES | 49950 | 5550 | 5550 | 38850 | Nissan | Ultima | 2004 | Y |
| 204294 | 7 | 21 | 16-11-1991 | IN | 500/1000 | 1000 | 1342.72 | 0 | 445638 | MALE | ... | 2 | ? | 62460 | 6940 | 6940 | 48580 | Honda | Accord | 2003 | N |
| 751612 | 297 | 48 | 22-06-2009 | IN | 250/500 | 1000 | 1464.73 | 3000000 | 443861 | MALE | ... | 0 | NO | 51480 | 5720 | 5720 | 40040 | Toyota | Highlander | 2013 | N |
| 756981 | 406 | 58 | 02-10-2003 | OH | 250/500 | 2000 | 1117.04 | 0 | 605121 | MALE | ... | 2 | ? | 65520 | 10920 | 5460 | 49140 | Volkswagen | Jetta | 2009 | N |
| 463727 | 6 | 27 | 05-08-1992 | OH | 250/500 | 500 | 1075.71 | 0 | 604328 | FEMALE | ... | 1 | YES | 3190 | 580 | 290 | 2320 | Saab | 95 | 2015 | N |
| 689901 | 344 | 51 | 28-04-1992 | IN | 100/300 | 2000 | 1398.46 | 0 | 455672 | MALE | ... | 2 | NO | 82830 | 7530 | 15060 | 60240 | Audi | A5 | 2004 | N |
| 396224 | 65 | 30 | 08-09-2009 | IN | 100/300 | 500 | 1455.65 | 4000000 | 616706 | FEMALE | ... | 3 | NO | 79800 | 15960 | 7980 | 55860 | Honda | Civic | 1999 | N |
| 682178 | 280 | 41 | 18-12-1994 | OH | 500/1000 | 2000 | 1140.31 | 0 | 473243 | MALE | ... | 3 | YES | 53020 | 9640 | 9640 | 33740 | Toyota | Corolla | 1999 | N |
| 596298 | 269 | 45 | 23-08-1996 | IN | 500/1000 | 500 | 1330.46 | 0 | 435552 | FEMALE | ... | 0 | NO | 24200 | 2200 | 4400 | 17600 | Suburu | Forrestor | 2008 | N |
| 253005 | 275 | 40 | 20-11-1991 | OH | 250/500 | 2000 | 1190.60 | 0 | 434206 | MALE | ... | 3 | YES | 43230 | 7860 | 7860 | 27510 | Chevrolet | Silverado | 2001 | N |
| 631565 | 283 | 43 | 14-07-1997 | IN | 100/300 | 2000 | 1161.91 | 0 | 457722 | FEMALE | ... | 3 | NO | 5850 | 1300 | 650 | 3900 | BMW | M5 | 2006 | N |
| 302669 | 56 | 29 | 29-06-2006 | IL | 100/300 | 1000 | 1523.17 | 0 | 610479 | MALE | ... | 2 | YES | 94560 | 7880 | 15760 | 70920 | Jeep | Grand Cherokee | 1995 | N |
| 971295 | 328 | 49 | 01-10-2001 | OH | 500/1000 | 500 | 1434.51 | 0 | 460535 | FEMALE | ... | 2 | YES | 71440 | 8930 | 8930 | 53580 | Mercedes | ML350 | 2005 | N |
| 525224 | 147 | 37 | 02-10-1992 | IN | 250/500 | 1000 | 1306.78 | 0 | 466818 | MALE | ... | 0 | NO | 81120 | 13520 | 20280 | 47320 | Toyota | Camry | 1995 | N |
| 355085 | 8 | 21 | 09-10-2012 | IN | 500/1000 | 500 | 1021.90 | 0 | 464237 | MALE | ... | 0 | ? | 91260 | 14040 | 14040 | 63180 | Toyota | Corolla | 2012 | N |
| 830729 | 297 | 48 | 10-02-1993 | IN | 100/300 | 1000 | 1538.60 | 0 | 618455 | FEMALE | ... | 0 | ? | 60600 | 6060 | 12120 | 42420 | Ford | Fusion | 2004 | N |
| 651948 | 150 | 31 | 28-09-1994 | IN | 500/1000 | 1000 | 1354.50 | 0 | 456602 | MALE | ... | 3 | YES | 64800 | 6480 | 12960 | 45360 | Suburu | Forrestor | 2000 | Y |
| 424358 | 4 | 34 | 24-05-2003 | OH | 500/1000 | 500 | 1282.93 | 0 | 616126 | FEMALE | ... | 0 | ? | 66880 | 6080 | 12160 | 48640 | Chevrolet | Silverado | 1996 | Y |
| 268833 | 91 | 31 | 18-09-1999 | IN | 100/300 | 1000 | 1338.40 | 4000000 | 431937 | FEMALE | ... | 0 | NO | 60570 | 6730 | 6730 | 47110 | Nissan | Maxima | 2011 | N |
| 287489 | 167 | 36 | 03-02-1994 | IL | 100/300 | 1000 | 949.44 | 0 | 448603 | FEMALE | ... | 0 | NO | 69680 | 8710 | 8710 | 52260 | Mercedes | ML350 | 2008 | Y |
| 497347 | 270 | 45 | 23-08-2003 | OH | 500/1000 | 500 | 1187.53 | 0 | 615383 | FEMALE | ... | 0 | YES | 54340 | 9880 | 0 | 44460 | Saab | 92x | 2005 | N |
| 847123 | 143 | 34 | 19-03-2014 | IL | 100/300 | 500 | 1442.27 | 0 | 435100 | MALE | ... | 3 | YES | 58500 | 11700 | 5850 | 40950 | Dodge | RAM | 1999 | N |
| 172307 | 146 | 32 | 06-12-1993 | OH | 100/300 | 2000 | 1276.43 | 0 | 431278 | MALE | ... | 3 | ? | 59940 | 6660 | 6660 | 46620 | Honda | CRV | 1995 | N |
| 432068 | 61 | 23 | 09-03-2007 | IL | 100/300 | 500 | 1111.72 | 0 | 448857 | MALE | ... | 2 | ? | 41850 | 4650 | 4650 | 32550 | Suburu | Legacy | 1997 | N |
| 903203 | 255 | 44 | 03-01-2004 | OH | 500/1000 | 2000 | 814.96 | 6000000 | 435267 | FEMALE | ... | 2 | NO | 6400 | 640 | 1280 | 4480 | Mercedes | ML350 | 2005 | Y |
| 506333 | 239 | 39 | 22-06-1990 | IL | 100/300 | 500 | 1396.83 | 0 | 442308 | FEMALE | ... | 3 | NO | 76890 | 6990 | 13980 | 55920 | BMW | X6 | 2007 | N |
| 722747 | 20 | 37 | 02-09-2011 | IL | 250/500 | 500 | 1482.14 | 0 | 613936 | FEMALE | ... | 1 | NO | 60750 | 6750 | 6750 | 47250 | Suburu | Forrestor | 2003 | N |
| 248467 | 126 | 30 | 06-10-2012 | IL | 250/500 | 2000 | 1171.75 | 0 | 472163 | FEMALE | ... | 3 | NO | 48730 | 4430 | 4430 | 39870 | Chevrolet | Malibu | 2011 | N |
| 910622 | 78 | 24 | 22-03-1992 | IN | 100/300 | 500 | 1175.51 | 0 | 474792 | MALE | ... | 1 | YES | 7480 | 680 | 680 | 6120 | Dodge | Neon | 2003 | N |
| 137675 | 123 | 28 | 03-12-2012 | IL | 100/300 | 2000 | 1836.02 | 0 | 470559 | MALE | ... | 1 | YES | 79800 | 13300 | 6650 | 59850 | Volkswagen | Passat | 2011 | Y |
| 758740 | 33 | 33 | 04-08-1997 | IL | 500/1000 | 1000 | 1096.79 | 6000000 | 446898 | FEMALE | ... | 1 | ? | 81400 | 8140 | 8140 | 65120 | BMW | M5 | 1998 | N |
| 574637 | 411 | 56 | 30-07-1992 | IL | 250/500 | 1000 | 1595.28 | 0 | 479320 | FEMALE | ... | 0 | ? | 38610 | 3510 | 3510 | 31590 | Volkswagen | Passat | 2007 | N |
| 373600 | 225 | 37 | 01-12-2000 | OH | 100/300 | 1000 | 1217.84 | 5000000 | 443462 | FEMALE | ... | 2 | NO | 57600 | 9600 | 9600 | 38400 | Volkswagen | Passat | 2015 | N |
| 396590 | 460 | 57 | 07-11-1997 | OH | 100/300 | 2000 | 1567.37 | 0 | 602514 | FEMALE | ... | 3 | NO | 58300 | 5830 | 11660 | 40810 | Nissan | Maxima | 2014 | Y |
| 218456 | 245 | 42 | 16-07-2002 | IL | 500/1000 | 1000 | 1575.74 | 7000000 | 614265 | MALE | ... | 2 | ? | 6490 | 590 | 1180 | 4720 | Toyota | Camry | 2011 | Y |
| 971338 | 380 | 56 | 04-11-2004 | OH | 100/300 | 1000 | 1570.86 | 0 | 454685 | MALE | ... | 1 | YES | 58160 | 7270 | 7270 | 43620 | Saab | 95 | 1996 | Y |
| 167231 | 66 | 28 | 26-01-1994 | IN | 100/300 | 2000 | 1472.77 | 0 | 471366 | MALE | ... | 1 | ? | 48290 | 8780 | 8780 | 30730 | Nissan | Maxima | 1995 | N |
| 807369 | 69 | 24 | 19-06-1992 | IN | 500/1000 | 500 | 1418.50 | 0 | 614948 | FEMALE | ... | 0 | NO | 63300 | 12660 | 6330 | 44310 | Dodge | RAM | 2012 | N |
| 203250 | 231 | 43 | 22-04-2010 | IN | 100/300 | 2000 | 1331.69 | 0 | 469653 | FEMALE | ... | 2 | NO | 66950 | 10300 | 10300 | 46350 | Chevrolet | Malibu | 2015 | N |
| 156636 | 261 | 46 | 10-09-2000 | IN | 100/300 | 1000 | 870.55 | 0 | 465631 | MALE | ... | 3 | ? | 80280 | 13380 | 13380 | 53520 | Chevrolet | Tahoe | 2013 | N |
| 284143 | 321 | 44 | 23-04-2008 | IL | 500/1000 | 2000 | 1344.56 | 6000000 | 443344 | MALE | ... | 2 | NO | 4680 | 520 | 0 | 4160 | Accura | MDX | 1999 | N |
| 740518 | 0 | 32 | 18-02-2011 | OH | 500/1000 | 1000 | 1377.04 | 0 | 441363 | MALE | ... | 1 | NO | 39720 | 6620 | 6620 | 26480 | Accura | MDX | 2002 | N |
| 445289 | 405 | 58 | 24-04-2012 | IL | 250/500 | 500 | 1237.88 | 0 | 462683 | MALE | ... | 0 | ? | 63580 | 5780 | 5780 | 52020 | Mercedes | ML350 | 1997 | Y |
| 357949 | 1 | 29 | 24-05-2006 | OH | 500/1000 | 500 | 854.58 | 0 | 612826 | FEMALE | ... | 3 | YES | 86790 | 7890 | 23670 | 55230 | Honda | CRV | 2003 | N |
| 231127 | 289 | 48 | 29-08-1995 | IL | 500/1000 | 500 | 1173.37 | 8000000 | 461744 | FEMALE | ... | 0 | ? | 42900 | 8580 | 0 | 34320 | Accura | TL | 1999 | N |
| 265093 | 83 | 24 | 01-01-2006 | IN | 500/1000 | 1000 | 1070.63 | 0 | 462106 | FEMALE | ... | 1 | NO | 61600 | 6160 | 12320 | 43120 | Honda | CRV | 2003 | N |
| 963680 | 283 | 48 | 04-01-2003 | OH | 500/1000 | 1000 | 1474.66 | 0 | 446755 | FEMALE | ... | 3 | NO | 6560 | 820 | 820 | 4920 | Volkswagen | Jetta | 2003 | N |
| 445694 | 194 | 34 | 24-05-2004 | IL | 250/500 | 1000 | 1193.45 | 0 | 464743 | MALE | ... | 1 | YES | 58800 | 11760 | 5880 | 41160 | Nissan | Pathfinder | 1997 | N |
| 215534 | 184 | 38 | 12-09-1994 | IL | 250/500 | 1000 | 1437.53 | 0 | 437889 | FEMALE | ... | 2 | NO | 53730 | 11940 | 5970 | 35820 | Dodge | RAM | 2013 | Y |
| 168260 | 284 | 48 | 01-03-1991 | OH | 250/500 | 1000 | 1168.80 | 0 | 444232 | FEMALE | ... | 3 | YES | 35750 | 6500 | 3250 | 26000 | Mercedes | E400 | 2001 | N |
| 538955 | 65 | 27 | 29-09-2001 | IN | 100/300 | 1000 | 1164.97 | 0 | 477695 | FEMALE | ... | 2 | YES | 42840 | 3570 | 7140 | 32130 | Chevrolet | Silverado | 2004 | N |
| 243226 | 222 | 39 | 10-01-2012 | IL | 250/500 | 1000 | 1232.72 | 0 | 458237 | MALE | ... | 0 | NO | 87960 | 14660 | 14660 | 58640 | Jeep | Wrangler | 1999 | Y |
| 505014 | 379 | 54 | 27-12-2001 | IL | 100/300 | 500 | 1251.16 | 0 | 447750 | FEMALE | ... | 1 | NO | 75960 | 6330 | 6330 | 63300 | Jeep | Grand Cherokee | 2010 | N |
| 534982 | 354 | 48 | 08-04-2003 | IL | 500/1000 | 2000 | 1526.11 | 5000000 | 469650 | FEMALE | ... | 3 | YES | 90240 | 15040 | 15040 | 60160 | Chevrolet | Malibu | 1995 | N |
| 110408 | 342 | 53 | 14-11-2005 | IN | 100/300 | 1000 | 1028.44 | 0 | 602304 | FEMALE | ... | 0 | NO | 80960 | 14720 | 7360 | 58880 | Accura | MDX | 2000 | N |
| 871432 | 254 | 46 | 15-07-2004 | IL | 250/500 | 2000 | 1472.43 | 0 | 619794 | MALE | ... | 3 | YES | 63580 | 5780 | 11560 | 46240 | Volkswagen | Jetta | 2004 | N |
| 689034 | 138 | 30 | 09-01-2002 | OH | 500/1000 | 500 | 1093.07 | 4000000 | 463291 | FEMALE | ... | 2 | NO | 83160 | 6930 | 13860 | 62370 | Volkswagen | Jetta | 2011 | N |
| 743092 | 239 | 41 | 11-11-2013 | OH | 250/500 | 1000 | 1325.44 | 7000000 | 474898 | FEMALE | ... | 2 | YES | 10790 | 1660 | 830 | 8300 | Mercedes | E400 | 2013 | N |
| 811042 | 131 | 29 | 04-07-2013 | IN | 250/500 | 1000 | 978.27 | 0 | 479821 | FEMALE | ... | 3 | NO | 76400 | 15280 | 7640 | 53480 | Suburu | Forrestor | 2003 | N |
| 505316 | 230 | 43 | 30-06-2002 | IN | 100/300 | 2000 | 1221.14 | 0 | 473394 | MALE | ... | 2 | ? | 75460 | 13720 | 13720 | 48020 | Audi | A5 | 2002 | N |
| 950880 | 210 | 38 | 19-12-1998 | IN | 250/500 | 500 | 999.52 | 0 | 615229 | MALE | ... | 2 | NO | 8640 | 1440 | 720 | 6480 | Accura | TL | 2008 | N |
| 788502 | 101 | 29 | 31-08-2014 | OH | 250/500 | 500 | 1380.89 | 0 | 620197 | MALE | ... | 1 | ? | 67210 | 12220 | 12220 | 42770 | BMW | X6 | 1996 | N |
| 627486 | 153 | 37 | 10-11-2005 | IN | 500/1000 | 500 | 1010.77 | 0 | 438215 | MALE | ... | 3 | NO | 42500 | 4250 | 4250 | 34000 | Volkswagen | Jetta | 1999 | N |
| 618682 | 185 | 35 | 04-03-2000 | IN | 500/1000 | 2000 | 1421.59 | 0 | 442695 | MALE | ... | 3 | YES | 4950 | 900 | 450 | 3600 | Ford | F150 | 2011 | N |
| 663938 | 194 | 38 | 26-01-2011 | IN | 100/300 | 2000 | 1231.25 | 0 | 604333 | FEMALE | ... | 0 | ? | 53680 | 4880 | 9760 | 39040 | Toyota | Camry | 2011 | N |
| 919875 | 27 | 27 | 29-06-2002 | IN | 100/300 | 2000 | 1118.76 | 0 | 470866 | FEMALE | ... | 3 | ? | 61560 | 6840 | 6840 | 47880 | Ford | Fusion | 2008 | N |
| 113464 | 180 | 33 | 19-04-2009 | IN | 500/1000 | 2000 | 1005.47 | 0 | 441871 | FEMALE | ... | 3 | YES | 57700 | 11540 | 5770 | 40390 | Jeep | Grand Cherokee | 2002 | N |
| 403776 | 371 | 54 | 27-04-2012 | IN | 100/300 | 2000 | 1317.97 | 0 | 469853 | MALE | ... | 2 | ? | 32280 | 5380 | 5380 | 21520 | Ford | Fusion | 2010 | Y |
| 502634 | 282 | 46 | 17-08-1991 | OH | 100/300 | 2000 | 1558.86 | 0 | 450800 | MALE | ... | 2 | NO | 69400 | 13880 | 6940 | 48580 | BMW | M5 | 2012 | N |
| 641934 | 51 | 34 | 25-12-2013 | OH | 500/1000 | 500 | 1430.80 | 0 | 461264 | MALE | ... | 3 | NO | 64200 | 6420 | 19260 | 38520 | Honda | Civic | 2007 | Y |
| 633090 | 96 | 27 | 17-02-2009 | IL | 100/300 | 1000 | 1631.10 | 0 | 437323 | FEMALE | ... | 2 | NO | 6030 | 670 | 670 | 4690 | Nissan | Pathfinder | 2007 | N |
| 464234 | 180 | 35 | 17-07-2005 | IL | 500/1000 | 1000 | 1252.48 | 0 | 432148 | MALE | ... | 3 | NO | 5100 | 1020 | 510 | 3570 | Chevrolet | Malibu | 2000 | N |
| 638155 | 463 | 59 | 03-08-1994 | IL | 250/500 | 1000 | 979.73 | 0 | 601848 | FEMALE | ... | 2 | NO | 72400 | 7240 | 14480 | 50680 | Chevrolet | Tahoe | 1999 | Y |
| 106186 | 75 | 25 | 02-12-2011 | IL | 500/1000 | 1000 | 1389.86 | 0 | 472475 | FEMALE | ... | 3 | YES | 65100 | 6510 | 6510 | 52080 | Saab | 93 | 2011 | N |
| 311783 | 193 | 40 | 25-02-2005 | OH | 100/300 | 500 | 1233.85 | 0 | 457463 | FEMALE | ... | 1 | YES | 64260 | 0 | 14280 | 49980 | Ford | Escape | 1999 | N |
| 528385 | 43 | 43 | 07-11-1997 | IL | 500/1000 | 500 | 1320.39 | 0 | 604861 | FEMALE | ... | 1 | ? | 79970 | 7270 | 21810 | 50890 | Honda | CRV | 1996 | Y |
| 710741 | 286 | 41 | 12-09-2001 | IL | 100/300 | 500 | 1106.77 | 0 | 603320 | FEMALE | ... | 0 | NO | 5000 | 500 | 500 | 4000 | Dodge | RAM | 2003 | N |
| 276804 | 3 | 29 | 27-11-1992 | IL | 100/300 | 500 | 995.70 | 5000000 | 615446 | FEMALE | ... | 1 | ? | 5000 | 500 | 1000 | 3500 | Mercedes | E400 | 2008 | Y |
| 507545 | 286 | 41 | 07-12-1998 | IL | 250/500 | 1000 | 1298.85 | 6000000 | 435967 | FEMALE | ... | 3 | YES | 54450 | 6050 | 12100 | 36300 | Saab | 92x | 2007 | N |
| 796375 | 64 | 29 | 22-10-2011 | OH | 250/500 | 2000 | 1202.28 | 0 | 479327 | MALE | ... | 2 | NO | 43700 | 4370 | 4370 | 34960 | Nissan | Pathfinder | 2007 | Y |
| 171183 | 98 | 31 | 01-02-1990 | IN | 100/300 | 500 | 671.92 | 0 | 468300 | MALE | ... | 0 | ? | 64080 | 7120 | 7120 | 49840 | Ford | Escape | 1997 | N |
| 143924 | 75 | 27 | 10-12-1993 | OH | 100/300 | 1000 | 1141.10 | 0 | 468515 | MALE | ... | 1 | YES | 71640 | 5970 | 11940 | 53730 | Toyota | Highlander | 2008 | N |
| 284834 | 110 | 27 | 03-08-2009 | OH | 500/1000 | 1000 | 1664.66 | 0 | 465921 | FEMALE | ... | 3 | ? | 57500 | 5750 | 5750 | 46000 | Audi | A3 | 2010 | N |
| 927205 | 137 | 30 | 16-12-2011 | IL | 250/500 | 500 | 1039.55 | 0 | 466393 | MALE | ... | 1 | ? | 74030 | 6730 | 13460 | 53840 | Chevrolet | Tahoe | 2005 | N |
| 655356 | 56 | 42 | 07-07-1996 | IL | 250/500 | 500 | 1339.39 | 0 | 471786 | FEMALE | ... | 2 | YES | 61490 | 11180 | 11180 | 39130 | BMW | X5 | 1998 | N |
| 432740 | 131 | 33 | 09-10-1990 | IL | 100/300 | 2000 | 1081.17 | 0 | 445120 | MALE | ... | 1 | NO | 4900 | 490 | 490 | 3920 | Toyota | Camry | 2010 | N |
| 893853 | 153 | 34 | 27-02-1994 | IL | 250/500 | 500 | 991.39 | 0 | 449260 | MALE | ... | 0 | NO | 77770 | 14140 | 14140 | 49490 | Nissan | Pathfinder | 2000 | N |
| 594988 | 255 | 43 | 06-05-2007 | IN | 500/1000 | 500 | 984.02 | 0 | 472724 | FEMALE | ... | 2 | ? | 74700 | 7470 | 14940 | 52290 | Nissan | Maxima | 2007 | Y |
| 191891 | 97 | 28 | 11-02-2010 | OH | 100/300 | 1000 | 830.31 | 0 | 443854 | MALE | ... | 0 | ? | 45270 | 10060 | 10060 | 25150 | Jeep | Wrangler | 2006 | N |
| 714346 | 438 | 57 | 05-10-1991 | OH | 500/1000 | 500 | 1119.29 | 0 | 616164 | FEMALE | ... | 0 | NO | 40700 | 4070 | 4070 | 32560 | Volkswagen | Passat | 2000 | Y |
| 326289 | 87 | 27 | 03-01-2004 | OH | 100/300 | 500 | 1048.39 | 0 | 620962 | FEMALE | ... | 1 | YES | 34650 | 6300 | 3150 | 25200 | Ford | F150 | 1996 | N |
| 944537 | 27 | 28 | 23-07-1992 | OH | 500/1000 | 1000 | 1074.47 | 0 | 465201 | MALE | ... | 0 | ? | 3200 | 400 | 400 | 2400 | Jeep | Grand Cherokee | 2012 | N |
| 779156 | 206 | 42 | 10-10-1993 | IL | 500/1000 | 1000 | 1230.76 | 0 | 470488 | MALE | ... | 1 | NO | 78980 | 7180 | 14360 | 57440 | Saab | 92x | 1997 | Y |
| 136520 | 228 | 40 | 01-03-1997 | IN | 100/300 | 500 | 1450.98 | 0 | 478609 | MALE | ... | 2 | NO | 6050 | 1100 | 1100 | 3850 | Toyota | Camry | 2010 | N |
| 912665 | 330 | 47 | 28-05-2014 | IL | 100/300 | 2000 | 1133.27 | 0 | 432218 | FEMALE | ... | 2 | YES | 60500 | 11000 | 5500 | 44000 | Ford | F150 | 1999 | Y |
| 469966 | 128 | 35 | 22-07-2004 | IN | 500/1000 | 500 | 1366.60 | 0 | 620493 | FEMALE | ... | 1 | NO | 88220 | 16040 | 16040 | 56140 | Chevrolet | Malibu | 2008 | N |
| 155912 | 140 | 35 | 21-03-2008 | OH | 100/300 | 1000 | 1520.78 | 0 | 470538 | FEMALE | ... | 2 | YES | 2860 | 520 | 260 | 2080 | Chevrolet | Tahoe | 1997 | Y |
| 409074 | 34 | 34 | 19-03-1992 | OH | 500/1000 | 500 | 1295.87 | 0 | 438529 | FEMALE | ... | 0 | NO | 50800 | 5080 | 5080 | 40640 | Audi | A3 | 1997 | Y |
| 824728 | 290 | 48 | 24-04-2013 | IL | 250/500 | 500 | 1161.03 | 5000000 | 469742 | MALE | ... | 1 | ? | 41520 | 5190 | 5190 | 31140 | Nissan | Ultima | 2014 | N |
| 606037 | 182 | 38 | 10-04-2009 | OH | 500/1000 | 2000 | 1441.06 | 0 | 435534 | FEMALE | ... | 3 | YES | 89650 | 8150 | 16300 | 65200 | Dodge | RAM | 2005 | N |
| 111874 | 143 | 32 | 05-07-2000 | IL | 500/1000 | 1000 | 1464.42 | 0 | 468986 | FEMALE | ... | 0 | NO | 62260 | 5660 | 5660 | 50940 | Saab | 92x | 1995 | N |
| 463513 | 254 | 40 | 23-04-1995 | IL | 250/500 | 500 | 1390.89 | 5000000 | 453719 | MALE | ... | 2 | ? | 53460 | 9720 | 4860 | 38880 | Volkswagen | Jetta | 2009 | N |
| 757352 | 112 | 32 | 21-12-1999 | OH | 500/1000 | 1000 | 1238.92 | 0 | 453400 | MALE | ... | 2 | ? | 5060 | 460 | 920 | 3680 | Honda | CRV | 2012 | N |
| 307469 | 16 | 32 | 28-07-2002 | IL | 100/300 | 1000 | 968.46 | 0 | 615767 | MALE | ... | 2 | ? | 59400 | 6600 | 13200 | 39600 | Dodge | RAM | 1995 | Y |
| 298412 | 107 | 32 | 06-05-2002 | OH | 100/300 | 500 | 1172.82 | 4000000 | 440680 | MALE | ... | 3 | NO | 3900 | 780 | 390 | 2730 | Ford | F150 | 2010 | N |
| 261905 | 252 | 39 | 28-02-2004 | IL | 500/1000 | 500 | 1312.22 | 9000000 | 609949 | MALE | ... | 3 | NO | 59400 | 11880 | 5940 | 41580 | Jeep | Grand Cherokee | 2010 | Y |
| 674485 | 303 | 43 | 14-01-1999 | OH | 500/1000 | 1000 | 671.01 | 7000000 | 479655 | FEMALE | ... | 0 | ? | 60210 | 6690 | 6690 | 46830 | Nissan | Maxima | 2013 | N |
| 223404 | 101 | 32 | 23-01-2002 | IL | 250/500 | 500 | 895.14 | 0 | 439964 | MALE | ... | 3 | YES | 43600 | 8720 | 4360 | 30520 | Suburu | Legacy | 2010 | N |
| 941807 | 316 | 46 | 25-06-2011 | OH | 100/300 | 500 | 1219.94 | 7000000 | 431968 | FEMALE | ... | 1 | YES | 63100 | 6310 | 12620 | 44170 | Accura | TL | 2000 | N |
| 437442 | 344 | 51 | 27-06-2008 | IL | 100/300 | 1000 | 959.83 | 0 | 435809 | FEMALE | ... | 2 | YES | 75400 | 17400 | 11600 | 46400 | BMW | X6 | 2006 | Y |
| 730973 | 209 | 36 | 11-01-2010 | IN | 100/300 | 2000 | 1223.39 | 0 | 452218 | FEMALE | ... | 3 | ? | 65440 | 8180 | 8180 | 49080 | Jeep | Wrangler | 2014 | N |
| 998865 | 80 | 28 | 05-12-2014 | IL | 500/1000 | 1000 | 1740.57 | 0 | 442142 | FEMALE | ... | 1 | ? | 33480 | 3720 | 3720 | 26040 | Dodge | Neon | 2011 | N |
| 749325 | 276 | 45 | 22-03-2000 | IL | 500/1000 | 500 | 948.10 | 0 | 430621 | FEMALE | ... | 2 | ? | 69300 | 13860 | 6930 | 48510 | Ford | Escape | 2010 | N |
| 698470 | 222 | 38 | 17-06-2008 | IN | 100/300 | 2000 | 1157.97 | 0 | 433853 | MALE | ... | 2 | YES | 60480 | 6720 | 6720 | 47040 | Accura | TL | 2001 | N |
| 238196 | 194 | 41 | 15-02-1993 | IL | 250/500 | 500 | 1203.81 | 0 | 613119 | MALE | ... | 2 | ? | 95900 | 13700 | 20550 | 61650 | Saab | 95 | 1999 | N |
| 206004 | 199 | 38 | 26-09-1991 | IL | 250/500 | 1000 | 1281.25 | 0 | 467780 | FEMALE | ... | 2 | NO | 44440 | 8080 | 4040 | 32320 | BMW | X6 | 2007 | Y |
| 654974 | 73 | 30 | 10-05-2009 | OH | 100/300 | 500 | 803.36 | 0 | 435371 | FEMALE | ... | 0 | YES | 57000 | 0 | 11400 | 45600 | Audi | A3 | 2013 | N |
| 641845 | 176 | 36 | 30-03-1995 | OH | 250/500 | 500 | 1865.83 | 5000000 | 436173 | MALE | ... | 1 | YES | 47300 | 4300 | 8600 | 34400 | Volkswagen | Jetta | 2006 | N |
| 794534 | 145 | 37 | 14-12-1991 | OH | 250/500 | 2000 | 1434.27 | 0 | 457234 | FEMALE | ... | 3 | ? | 55110 | 5010 | 10020 | 40080 | Nissan | Maxima | 2002 | N |
| 639027 | 270 | 41 | 21-06-1994 | IL | 250/500 | 1000 | 817.28 | 0 | 460263 | MALE | ... | 1 | NO | 60190 | 4630 | 9260 | 46300 | Mercedes | ML350 | 2014 | Y |
| 669800 | 456 | 62 | 24-06-2009 | OH | 250/500 | 1000 | 1395.77 | 0 | 611651 | FEMALE | ... | 3 | NO | 66480 | 5540 | 11080 | 49860 | Saab | 92x | 2012 | Y |
| 153298 | 130 | 34 | 23-03-2009 | OH | 100/300 | 500 | 990.11 | 0 | 442666 | MALE | ... | 3 | YES | 5830 | 1060 | 1060 | 3710 | Dodge | RAM | 2015 | N |
| 804751 | 189 | 39 | 11-09-1997 | OH | 250/500 | 2000 | 838.02 | 0 | 450702 | FEMALE | ... | 0 | YES | 64400 | 6440 | 6440 | 51520 | Dodge | Neon | 1997 | N |
| 713172 | 140 | 32 | 23-10-1996 | IL | 250/500 | 1000 | 985.97 | 5000000 | 457793 | FEMALE | ... | 3 | ? | 82170 | 14940 | 7470 | 59760 | Chevrolet | Silverado | 1995 | Y |
| 621756 | 243 | 41 | 21-04-1997 | IN | 100/300 | 1000 | 1129.23 | 0 | 470190 | FEMALE | ... | 0 | YES | 50300 | 10060 | 5030 | 35210 | Suburu | Legacy | 1999 | Y |
| 947598 | 219 | 43 | 20-06-2002 | IN | 100/300 | 1000 | 1114.29 | 0 | 465136 | FEMALE | ... | 2 | YES | 66660 | 6060 | 6060 | 54540 | Toyota | Highlander | 2006 | N |
| 374545 | 149 | 34 | 28-08-2005 | IN | 250/500 | 500 | 664.86 | 0 | 608963 | FEMALE | ... | 1 | NO | 105040 | 16160 | 16160 | 72720 | Dodge | RAM | 1999 | N |
| 180286 | 160 | 33 | 08-02-2009 | IL | 500/1000 | 1000 | 1422.78 | 0 | 616583 | FEMALE | ... | 3 | YES | 52800 | 5280 | 5280 | 42240 | Nissan | Pathfinder | 2006 | N |
| 662088 | 224 | 42 | 06-03-2005 | OH | 500/1000 | 500 | 1212.75 | 0 | 455913 | FEMALE | ... | 0 | YES | 55200 | 9200 | 13800 | 32200 | Honda | Civic | 1998 | N |
| 178081 | 385 | 51 | 20-07-1990 | IN | 250/500 | 1000 | 976.37 | 0 | 602842 | FEMALE | ... | 3 | ? | 67600 | 13520 | 6760 | 47320 | Suburu | Legacy | 2007 | N |
| 398683 | 373 | 55 | 30-04-2007 | IN | 250/500 | 500 | 1607.36 | 0 | 444626 | MALE | ... | 2 | NO | 60600 | 6060 | 12120 | 42420 | Dodge | RAM | 2007 | Y |
| 786432 | 435 | 58 | 15-11-1997 | IN | 100/300 | 2000 | 1145.85 | 0 | 471784 | MALE | ... | 1 | YES | 41490 | 9220 | 4610 | 27660 | Mercedes | E400 | 2004 | N |
| 938634 | 144 | 35 | 30-08-1993 | IL | 100/300 | 500 | 1427.46 | 0 | 444922 | MALE | ... | 0 | ? | 87900 | 17580 | 8790 | 61530 | Dodge | Neon | 1995 | N |
| 482495 | 92 | 32 | 29-01-1998 | IL | 500/1000 | 500 | 1592.41 | 0 | 474324 | MALE | ... | 3 | YES | 53400 | 5340 | 5340 | 42720 | Jeep | Wrangler | 1996 | N |
| 327488 | 155 | 35 | 09-08-1993 | OH | 250/500 | 1000 | 919.37 | 0 | 459537 | FEMALE | ... | 3 | ? | 48360 | 8060 | 8060 | 32240 | Nissan | Maxima | 1997 | N |
| 715202 | 78 | 31 | 02-04-1991 | OH | 250/500 | 1000 | 1377.23 | 0 | 440757 | FEMALE | ... | 1 | ? | 52290 | 5810 | 11620 | 34860 | Nissan | Maxima | 1997 | N |
| 516959 | 264 | 43 | 01-05-2010 | IL | 100/300 | 500 | 1508.12 | 6000000 | 433275 | MALE | ... | 1 | NO | 60750 | 13500 | 6750 | 40500 | Jeep | Wrangler | 2015 | Y |
| 984456 | 66 | 30 | 24-06-2003 | IN | 500/1000 | 500 | 484.67 | 0 | 608309 | FEMALE | ... | 2 | YES | 65560 | 11920 | 11920 | 41720 | Volkswagen | Passat | 2015 | Y |
| 786103 | 188 | 37 | 24-09-1994 | OH | 100/300 | 500 | 1457.21 | 0 | 471785 | FEMALE | ... | 0 | YES | 45000 | 5000 | 5000 | 35000 | Suburu | Forrestor | 2003 | N |
| 185124 | 169 | 37 | 07-12-2001 | IL | 100/300 | 1000 | 936.19 | 0 | 454776 | MALE | ... | 1 | YES | 57900 | 17370 | 5790 | 34740 | Audi | A5 | 2005 | N |
| 362407 | 209 | 39 | 06-12-1996 | IN | 100/300 | 500 | 1264.99 | 0 | 614169 | MALE | ... | 1 | NO | 37800 | 8400 | 4200 | 25200 | Chevrolet | Silverado | 1995 | N |
| 389525 | 210 | 37 | 10-07-2012 | OH | 500/1000 | 500 | 1467.76 | 0 | 601425 | FEMALE | ... | 3 | YES | 63300 | 6330 | 6330 | 50640 | Toyota | Highlander | 2000 | N |
| 488724 | 239 | 40 | 29-11-2004 | IN | 100/300 | 500 | 1463.95 | 0 | 430567 | FEMALE | ... | 0 | YES | 69740 | 6340 | 6340 | 57060 | Dodge | Neon | 2003 | N |
| 338070 | 446 | 61 | 25-01-2006 | IN | 500/1000 | 1000 | 1037.32 | 0 | 438837 | FEMALE | ... | 1 | NO | 80880 | 6740 | 13480 | 60660 | Nissan | Ultima | 2005 | N |
| 865607 | 476 | 61 | 18-04-1993 | IN | 250/500 | 1000 | 1562.80 | 0 | 458997 | FEMALE | ... | 2 | YES | 49390 | 8980 | 4490 | 35920 | Suburu | Legacy | 2009 | N |
| 725330 | 169 | 34 | 21-07-1996 | IN | 100/300 | 500 | 1469.75 | 0 | 458132 | FEMALE | ... | 0 | YES | 38700 | 7740 | 3870 | 27090 | Volkswagen | Passat | 2012 | N |
| 272330 | 297 | 47 | 29-11-2009 | IN | 250/500 | 500 | 1616.65 | 7000000 | 456363 | MALE | ... | 3 | YES | 44400 | 5550 | 5550 | 33300 | Jeep | Grand Cherokee | 1999 | N |
| 728025 | 421 | 56 | 15-02-1990 | IN | 100/300 | 500 | 1935.85 | 4000000 | 470826 | MALE | ... | 3 | ? | 92730 | 16860 | 8430 | 67440 | Mercedes | E400 | 2004 | Y |
| 753056 | 140 | 36 | 03-05-1991 | IN | 250/500 | 500 | 1363.59 | 0 | 468303 | FEMALE | ... | 0 | YES | 79500 | 7950 | 7950 | 63600 | Chevrolet | Tahoe | 2000 | N |
| 910365 | 200 | 34 | 19-12-2001 | IN | 250/500 | 1000 | 828.42 | 3000000 | 467762 | FEMALE | ... | 2 | NO | 56000 | 5600 | 5600 | 44800 | Chevrolet | Malibu | 2009 | N |
| 824116 | 135 | 34 | 05-05-1998 | IL | 250/500 | 2000 | 1687.53 | 0 | 465674 | FEMALE | ... | 2 | NO | 78200 | 15640 | 7820 | 54740 | Audi | A3 | 2009 | N |
| 322613 | 110 | 33 | 16-04-1995 | IN | 250/500 | 1000 | 1183.48 | 0 | 442389 | MALE | ... | 3 | NO | 55200 | 13800 | 9200 | 32200 | Saab | 93 | 2015 | Y |
| 886473 | 282 | 48 | 10-03-1991 | OH | 500/1000 | 2000 | 1422.56 | 7000000 | 473705 | FEMALE | ... | 2 | NO | 3520 | 640 | 320 | 2560 | Accura | MDX | 2013 | N |
| 833321 | 215 | 38 | 01-03-2010 | IN | 250/500 | 500 | 1405.71 | 0 | 465376 | FEMALE | ... | 1 | NO | 70700 | 7070 | 14140 | 49490 | Volkswagen | Passat | 2008 | N |
| 521592 | 140 | 30 | 15-06-2014 | IL | 100/300 | 500 | 1354.20 | 0 | 438775 | FEMALE | ... | 0 | ? | 60170 | 5470 | 10940 | 43760 | Nissan | Pathfinder | 2006 | N |
| 254837 | 250 | 42 | 25-11-2004 | IN | 100/300 | 500 | 1055.60 | 0 | 457962 | MALE | ... | 1 | ? | 74800 | 13600 | 6800 | 54400 | Ford | Fusion | 2009 | Y |
| 476839 | 65 | 29 | 09-08-1990 | IL | 250/500 | 1000 | 1726.91 | 0 | 456570 | MALE | ... | 0 | ? | 7200 | 720 | 1440 | 5040 | Audi | A5 | 1999 | N |
| 149601 | 187 | 34 | 28-03-2003 | IN | 500/1000 | 500 | 1232.57 | 0 | 612986 | FEMALE | ... | 0 | ? | 45100 | 8200 | 4100 | 32800 | Nissan | Pathfinder | 2011 | N |
| 630683 | 386 | 53 | 23-10-2007 | OH | 250/500 | 500 | 1078.65 | 0 | 615730 | MALE | ... | 3 | YES | 66660 | 12120 | 6060 | 48480 | Honda | Civic | 2006 | N |
| 569245 | 293 | 49 | 05-12-1995 | IL | 100/300 | 2000 | 1239.06 | 0 | 439360 | FEMALE | ... | 1 | ? | 57310 | 5210 | 10420 | 41680 | Volkswagen | Passat | 2002 | N |
| 907012 | 179 | 32 | 15-12-1996 | OH | 500/1000 | 2000 | 1246.68 | 0 | 440251 | FEMALE | ... | 1 | ? | 53100 | 5900 | 5900 | 41300 | Suburu | Impreza | 2006 | N |
| 528259 | 370 | 55 | 22-12-2012 | IN | 500/1000 | 2000 | 1389.13 | 7000000 | 456203 | MALE | ... | 2 | ? | 9000 | 900 | 1800 | 6300 | Mercedes | ML350 | 2015 | N |
| 728839 | 233 | 39 | 02-01-2001 | OH | 500/1000 | 2000 | 1524.18 | 0 | 605220 | MALE | ... | 0 | YES | 48870 | 5430 | 5430 | 38010 | Saab | 95 | 1999 | N |
| 264221 | 297 | 48 | 28-07-2014 | IL | 500/1000 | 1000 | 1243.68 | 0 | 463331 | MALE | ... | 2 | ? | 54960 | 6870 | 0 | 48090 | Toyota | Corolla | 2002 | Y |
| 672416 | 147 | 37 | 20-04-2013 | IN | 500/1000 | 2000 | 1375.29 | 0 | 609226 | FEMALE | ... | 1 | ? | 56160 | 6240 | 6240 | 43680 | Ford | Fusion | 2015 | Y |
| 521854 | 198 | 36 | 16-02-2001 | IN | 250/500 | 1000 | 1096.39 | 0 | 603848 | MALE | ... | 3 | YES | 49410 | 5490 | 5490 | 38430 | Audi | A3 | 2015 | N |
| 737252 | 290 | 45 | 18-11-1993 | OH | 500/1000 | 2000 | 1215.36 | 0 | 617739 | MALE | ... | 1 | NO | 66200 | 6620 | 6620 | 52960 | Suburu | Impreza | 2012 | N |
| 898519 | 233 | 43 | 21-05-2000 | OH | 250/500 | 1000 | 954.18 | 0 | 437470 | FEMALE | ... | 3 | YES | 42500 | 8500 | 4250 | 29750 | Nissan | Pathfinder | 2000 | N |
| 101421 | 91 | 26 | 19-10-1999 | IL | 250/500 | 1000 | 1022.46 | 0 | 444896 | FEMALE | ... | 2 | ? | 74200 | 7420 | 7420 | 59360 | Jeep | Wrangler | 1996 | N |
| 737483 | 218 | 43 | 14-02-1996 | IL | 250/500 | 500 | 1521.55 | 0 | 477856 | FEMALE | ... | 3 | YES | 68200 | 13640 | 6820 | 47740 | Dodge | RAM | 2003 | Y |
| 979963 | 260 | 43 | 03-06-2009 | IN | 100/300 | 500 | 982.22 | 0 | 604279 | FEMALE | ... | 0 | ? | 75400 | 15080 | 7540 | 52780 | Honda | CRV | 2011 | N |
| 130156 | 322 | 49 | 24-09-2001 | IL | 250/500 | 2000 | 1277.12 | 0 | 431853 | FEMALE | ... | 2 | YES | 42240 | 7680 | 7680 | 26880 | Chevrolet | Malibu | 2007 | N |
| 676255 | 405 | 57 | 28-12-1999 | IN | 500/1000 | 1000 | 1132.47 | 4000000 | 434293 | MALE | ... | 3 | ? | 61440 | 10240 | 10240 | 40960 | Saab | 93 | 2008 | N |
| 815883 | 398 | 55 | 02-07-1991 | OH | 250/500 | 2000 | 1305.26 | 0 | 455482 | MALE | ... | 3 | YES | 36740 | 3340 | 6680 | 26720 | BMW | X5 | 1998 | Y |
| 258265 | 9 | 30 | 10-04-1994 | IL | 100/300 | 1000 | 1073.83 | 0 | 438877 | FEMALE | ... | 0 | NO | 85690 | 15580 | 15580 | 54530 | BMW | 3 Series | 2011 | N |
| 698589 | 479 | 60 | 28-11-2002 | IL | 500/1000 | 1000 | 1188.45 | 0 | 459295 | FEMALE | ... | 3 | ? | 53900 | 5390 | 10780 | 37730 | Saab | 95 | 2006 | N |
| 330119 | 215 | 35 | 15-06-2004 | IL | 500/1000 | 1000 | 1125.40 | 0 | 606144 | MALE | ... | 1 | NO | 2640 | 220 | 440 | 1980 | Jeep | Wrangler | 2001 | N |
| 927354 | 45 | 31 | 15-09-1990 | IN | 100/300 | 500 | 1459.50 | 0 | 475891 | MALE | ... | 3 | ? | 6000 | 1000 | 1000 | 4000 | Suburu | Impreza | 2000 | N |
| 231508 | 156 | 38 | 16-09-2009 | IL | 100/300 | 500 | 1367.99 | 0 | 462525 | MALE | ... | 3 | ? | 55200 | 11040 | 5520 | 38640 | Saab | 92x | 1998 | Y |
| 868031 | 425 | 53 | 24-06-1990 | OH | 250/500 | 2000 | 912.29 | 0 | 464808 | FEMALE | ... | 2 | ? | 97080 | 16180 | 16180 | 64720 | Saab | 92x | 2005 | N |
| 744557 | 192 | 38 | 25-02-2011 | IN | 500/1000 | 1000 | 1281.43 | 0 | 432405 | FEMALE | ... | 2 | NO | 30700 | 3070 | 6140 | 21490 | Jeep | Wrangler | 2010 | N |
| 727109 | 98 | 26 | 20-02-2001 | IN | 500/1000 | 2000 | 1082.10 | 0 | 477268 | MALE | ... | 1 | YES | 65430 | 14540 | 7270 | 43620 | Jeep | Wrangler | 2001 | N |
| 608443 | 259 | 45 | 21-12-2006 | IL | 500/1000 | 2000 | 1175.07 | 0 | 457121 | MALE | ... | 1 | NO | 87780 | 7980 | 7980 | 71820 | Honda | CRV | 2011 | N |
| 727443 | 186 | 38 | 01-07-2013 | OH | 100/300 | 500 | 922.85 | 0 | 471148 | MALE | ... | 1 | YES | 3600 | 400 | 400 | 2800 | Honda | Civic | 1999 | N |
| 398484 | 277 | 46 | 07-11-1992 | IL | 250/500 | 2000 | 1177.57 | 0 | 469220 | FEMALE | ... | 3 | NO | 3870 | 430 | 860 | 2580 | Jeep | Wrangler | 2010 | N |
| 752504 | 211 | 38 | 15-05-1997 | IN | 250/500 | 1000 | 1055.09 | 0 | 433250 | FEMALE | ... | 3 | YES | 91520 | 8320 | 16640 | 66560 | BMW | X6 | 2005 | Y |
| 967713 | 243 | 44 | 25-12-1997 | IL | 250/500 | 500 | 809.11 | 0 | 600208 | MALE | ... | 1 | YES | 51400 | 5140 | 10280 | 35980 | Honda | Civic | 1996 | N |
| 840225 | 154 | 34 | 05-10-1999 | OH | 100/300 | 1000 | 1041.36 | 0 | 448436 | FEMALE | ... | 3 | YES | 74360 | 13520 | 13520 | 47320 | Toyota | Highlander | 2005 | Y |
| 643226 | 204 | 40 | 07-04-1992 | OH | 250/500 | 1000 | 1693.83 | 7000000 | 447976 | MALE | ... | 0 | ? | 53400 | 5340 | 5340 | 42720 | Honda | CRV | 2003 | N |
| 535879 | 458 | 59 | 05-03-2009 | IN | 100/300 | 1000 | 1685.69 | 0 | 472236 | FEMALE | ... | 2 | YES | 71800 | 14360 | 14360 | 43080 | Jeep | Grand Cherokee | 1995 | N |
| 746630 | 147 | 31 | 10-02-1997 | IN | 250/500 | 500 | 1054.92 | 6000000 | 468232 | FEMALE | ... | 0 | ? | 68240 | 8530 | 0 | 59710 | Toyota | Corolla | 2013 | Y |
| 598308 | 279 | 45 | 28-01-1992 | IN | 250/500 | 2000 | 1333.97 | 6000000 | 620819 | FEMALE | ... | 0 | ? | 61050 | 11100 | 11100 | 38850 | Dodge | RAM | 2011 | Y |
| 720356 | 308 | 47 | 16-09-2013 | OH | 100/300 | 1000 | 1013.61 | 6000000 | 452349 | FEMALE | ... | 1 | YES | 5590 | 860 | 860 | 3870 | Suburu | Impreza | 2002 | N |
| 724752 | 284 | 48 | 16-05-2008 | IL | 500/1000 | 500 | 958.30 | 0 | 464646 | FEMALE | ... | 0 | ? | 46860 | 8520 | 8520 | 29820 | Volkswagen | Passat | 1998 | N |
| 231548 | 31 | 32 | 07-09-1999 | IL | 100/300 | 2000 | 1263.48 | 4000000 | 442948 | FEMALE | ... | 0 | ? | 63600 | 5300 | 10600 | 47700 | Audi | A5 | 1997 | Y |
| 193213 | 407 | 55 | 11-03-1996 | OH | 100/300 | 1000 | 1250.08 | 5000000 | 474598 | FEMALE | ... | 3 | YES | 68160 | 11360 | 11360 | 45440 | Ford | Escape | 2010 | N |
| 125591 | 187 | 37 | 08-08-2013 | IN | 500/1000 | 1000 | 1412.06 | 5000000 | 450947 | FEMALE | ... | 3 | ? | 57700 | 5770 | 5770 | 46160 | Nissan | Maxima | 2000 | N |
| 437960 | 128 | 35 | 03-04-2001 | IN | 250/500 | 1000 | 1074.99 | 0 | 453620 | FEMALE | ... | 0 | ? | 7590 | 1380 | 690 | 5520 | Accura | MDX | 2012 | N |
| 390256 | 163 | 37 | 25-11-2009 | IN | 500/1000 | 1000 | 1200.33 | 4000000 | 477631 | FEMALE | ... | 1 | YES | 3900 | 390 | 780 | 2730 | Volkswagen | Jetta | 2008 | Y |
| 488597 | 80 | 26 | 08-05-2001 | IL | 100/300 | 1000 | 1343.00 | 0 | 443625 | MALE | ... | 0 | NO | 90400 | 9040 | 9040 | 72320 | BMW | 3 Series | 1995 | N |
| 844062 | 398 | 55 | 25-05-1990 | OH | 250/500 | 500 | 862.19 | 0 | 606858 | MALE | ... | 3 | ? | 6600 | 600 | 1200 | 4800 | Accura | MDX | 2012 | N |
| 221854 | 232 | 44 | 03-10-1994 | OH | 250/500 | 2000 | 1181.64 | 0 | 454552 | MALE | ... | 1 | YES | 55400 | 5540 | 11080 | 38780 | Jeep | Grand Cherokee | 2002 | Y |
| 889764 | 143 | 33 | 30-11-1993 | OH | 500/1000 | 1000 | 1200.09 | 0 | 454191 | FEMALE | ... | 2 | ? | 70400 | 14080 | 7040 | 49280 | Accura | RSX | 2002 | N |
| 929306 | 266 | 42 | 06-03-2003 | IN | 100/300 | 500 | 1093.83 | 4000000 | 468454 | MALE | ... | 1 | NO | 53280 | 4440 | 8880 | 39960 | Suburu | Impreza | 2015 | Y |
| 515457 | 89 | 32 | 18-12-1996 | IN | 250/500 | 1000 | 988.93 | 0 | 614187 | FEMALE | ... | 3 | YES | 84590 | 15380 | 15380 | 53830 | Dodge | Neon | 1999 | N |
| 525862 | 50 | 44 | 18-10-2000 | OH | 250/500 | 2000 | 1188.51 | 0 | 447469 | MALE | ... | 2 | NO | 61100 | 6110 | 12220 | 42770 | Dodge | Neon | 2008 | N |
| 490514 | 230 | 43 | 09-02-2007 | IN | 500/1000 | 2000 | 1101.83 | 0 | 451529 | MALE | ... | 3 | YES | 51900 | 5190 | 10380 | 36330 | BMW | M5 | 2011 | Y |
| 774895 | 17 | 39 | 28-10-2006 | IL | 250/500 | 1000 | 840.95 | 0 | 431202 | FEMALE | ... | 1 | ? | 3440 | 430 | 430 | 2580 | Suburu | Legacy | 2002 | N |
| 669809 | 29 | 32 | 05-04-2002 | OH | 100/300 | 1000 | 1722.50 | 0 | 453713 | MALE | ... | 2 | ? | 76900 | 7690 | 7690 | 61520 | Jeep | Wrangler | 1995 | N |
| 836349 | 235 | 39 | 01-05-2013 | IL | 500/1000 | 2000 | 1453.61 | 4000000 | 619570 | MALE | ... | 3 | ? | 60320 | 9280 | 9280 | 41760 | Chevrolet | Tahoe | 2012 | Y |
| 663190 | 286 | 43 | 05-02-1994 | IL | 100/300 | 500 | 1564.43 | 3000000 | 477644 | FEMALE | ... | 2 | YES | 34290 | 3810 | 3810 | 26670 | Jeep | Grand Cherokee | 2013 | N |
| 674570 | 124 | 28 | 08-12-2001 | OH | 250/500 | 1000 | 1235.14 | 0 | 443567 | MALE | ... | 1 | ? | 60200 | 6020 | 6020 | 48160 | Volkswagen | Passat | 2012 | N |
| 681486 | 141 | 30 | 24-03-2007 | IN | 500/1000 | 1000 | 1347.04 | 0 | 430665 | MALE | ... | 2 | YES | 6480 | 540 | 1080 | 4860 | Honda | Civic | 1996 | N |
| 918516 | 130 | 34 | 17-02-2003 | OH | 250/500 | 500 | 1383.49 | 3000000 | 442797 | FEMALE | ... | 3 | YES | 67500 | 7500 | 7500 | 52500 | Suburu | Impreza | 1996 | N |
| 533940 | 458 | 62 | 18-11-2011 | IL | 500/1000 | 2000 | 1356.92 | 5000000 | 441714 | MALE | ... | 1 | YES | 46980 | 5220 | 5220 | 36540 | Audi | A5 | 1998 | N |
| 556080 | 456 | 60 | 11-11-1996 | OH | 250/500 | 1000 | 766.19 | 0 | 612260 | FEMALE | ... | 3 | ? | 5060 | 460 | 920 | 3680 | Mercedes | E400 | 2007 | N |
360 rows × 38 columns
fraud_df.replace({'property_damage':'?'},{'property_damage':'Unknown'},inplace=True)
fraud_df.replace({'collision_type':'?'},{'collision_type':'Unknown'},inplace=True)
fraud_df.replace({'police_report_available':'?'},{'police_report_available':'Unknown'},inplace=True)
fraud_df[cat].describe().T
| count | unique | top | freq | |
|---|---|---|---|---|
| policy_bind_date | 1000 | 951 | 01-01-2006 | 3 |
| policy_state | 1000 | 3 | OH | 352 |
| policy_csl | 1000 | 3 | 250/500 | 351 |
| insured_sex | 1000 | 2 | FEMALE | 537 |
| insured_education_level | 1000 | 7 | JD | 161 |
| insured_occupation | 1000 | 14 | machine-op-inspct | 93 |
| insured_hobbies | 1000 | 20 | reading | 64 |
| insured_relationship | 1000 | 6 | own-child | 183 |
| incident_date | 1000 | 60 | 02-02-2015 | 28 |
| incident_type | 1000 | 4 | Multi-vehicle Collision | 419 |
| collision_type | 1000 | 4 | Rear Collision | 292 |
| incident_severity | 1000 | 4 | Minor Damage | 354 |
| authorities_contacted | 1000 | 5 | Police | 292 |
| incident_state | 1000 | 7 | NY | 262 |
| incident_city | 1000 | 7 | Springfield | 157 |
| incident_location | 1000 | 1000 | 3340 3rd Hwy | 1 |
| property_damage | 1000 | 3 | Unknown | 360 |
| police_report_available | 1000 | 3 | NO | 343 |
| auto_make | 1000 | 14 | Dodge | 80 |
| auto_model | 1000 | 39 | RAM | 43 |
| fraud_reported | 1000 | 2 | N | 753 |
plt.figure(figsize=(10,10))
fraud_df.boxplot(vert=0)
<AxesSubplot:>
# fraud_df_cp.drop('policy_bind_date',axis=1,inplace=True)
# fraud_df_cp.drop('incident_date',axis=1,inplace=True)
#fraud_df_X = fraud_df_cp.drop('fraud_reported',axis=1)
#fraud_df_Y = fraud_df_cp_upd['fraud_reported']
fraud_df_X = fraud_df.drop('fraud_reported',axis=1)
fraud_df_Y = fraud_df['fraud_reported']
fraud_df_X.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 1000 non-null int64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 1000 non-null float64 7 umbrella_limit 1000 non-null int64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null object 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 1000 non-null int64 31 injury_claim 1000 non-null int64 32 property_claim 1000 non-null int64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 dtypes: float64(1), int64(16), object(20) memory usage: 336.9+ KB
Q1 = fraud_df_X.quantile(0.25)
Q3=fraud_df_X.quantile(0.75)
IQR=Q3-Q1
UL = Q3+1.5*IQR
LL=Q1-1.5*IQR
((fraud_df_X > UL) | (fraud_df_X < LL)).sum()
age 4 authorities_contacted 0 auto_make 0 auto_model 0 auto_year 0 bodily_injuries 0 capital-gains 0 capital-loss 0 collision_type 0 incident_city 0 incident_date 0 incident_hour_of_the_day 0 incident_location 0 incident_severity 0 incident_state 0 incident_type 0 injury_claim 0 insured_education_level 0 insured_hobbies 0 insured_occupation 0 insured_relationship 0 insured_sex 0 insured_zip 0 months_as_customer 0 number_of_vehicles_involved 0 police_report_available 0 policy_annual_premium 9 policy_bind_date 0 policy_csl 0 policy_deductable 0 policy_state 0 property_claim 6 property_damage 0 total_claim_amount 1 umbrella_limit 201 vehicle_claim 0 witnesses 0 dtype: int64
fraud_df_X[((fraud_df_X > UL) | (fraud_df_X < LL))]=np.nan
fraud_df_X.isnull().sum()
months_as_customer 0 age 4 policy_bind_date 0 policy_state 0 policy_csl 0 policy_deductable 0 policy_annual_premium 9 umbrella_limit 201 insured_zip 0 insured_sex 0 insured_education_level 0 insured_occupation 0 insured_hobbies 0 insured_relationship 0 capital-gains 0 capital-loss 0 incident_date 0 incident_type 0 collision_type 0 incident_severity 0 authorities_contacted 0 incident_state 0 incident_city 0 incident_location 0 incident_hour_of_the_day 0 number_of_vehicles_involved 0 property_damage 0 bodily_injuries 0 witnesses 0 police_report_available 0 total_claim_amount 1 injury_claim 0 property_claim 6 vehicle_claim 0 auto_make 0 auto_model 0 auto_year 0 dtype: int64
fraud_df_X.isnull().sum().sum()
221
fraud_df_up = pd.concat([fraud_df_X,fraud_df_Y],axis=1)
plt.figure(figsize=(12,8))
sns.heatmap(fraud_df_up.isnull(),cbar=False,cmap='coolwarm',yticklabels=False)
plt.show()
fraud_df_up.isnull().sum(axis=1)
policy_number 521585 0 342868 1 687698 1 227811 1 367455 1 104594 0 413978 0 429027 0 485665 0 636550 0 543610 1 214618 1 842643 1 626808 0 644081 0 892874 0 558938 1 275265 1 921202 0 143972 0 183430 1 431876 0 285496 0 115399 0 736882 0 699044 0 863236 0 608513 1 914088 0 596785 0 908616 0 666333 1 336614 0 584859 0 990493 0 129872 1 200152 0 933293 0 485664 0 982871 0 206213 0 616337 0 448961 0 790442 1 108844 0 430029 0 529112 0 939631 1 866931 1 582011 0 691189 1 537546 0 394975 0 729634 0 282195 0 420810 0 524836 0 307195 0 623648 0 485372 0 598554 0 303987 0 343161 0 519312 0 132902 1 332867 0 356590 1 346002 1 500533 0 348209 0 486676 0 260845 0 657045 0 761189 0 175177 0 116700 0 166264 0 527945 0 627540 1 279422 0 484200 0 645258 1 694662 1 960680 0 498140 0 498875 0 798177 1 614763 0 679370 1 958857 0 686816 1 127754 1 918629 0 731450 0 307447 0 992145 1 900628 0 235220 0 740019 0 246882 0 797613 0 193442 0 389238 0 760179 0 939905 0 872814 0 632627 0 283414 0 163161 0 853360 0 776860 0 149367 1 395269 0 981123 0 143626 0 648397 1 154982 0 330591 0 319232 0 531640 1 368050 1 253791 1 155724 0 824540 0 717392 0 965768 1 414779 0 428230 0 517240 0 469874 1 718428 0 620215 0 618659 0 649082 1 437573 0 964657 0 932502 0 434507 0 935277 0 756054 0 682387 0 456604 0 139872 0 354105 1 165485 0 515050 0 795686 1 395983 0 119513 0 217938 0 203914 0 565157 0 904191 0 419510 0 575000 1 120485 0 781181 0 299796 1 589749 0 854021 0 454086 0 139484 1 678849 0 346940 1 985436 0 237418 0 335780 0 491392 0 140880 0 962591 0 922565 0 288580 0 154280 0 425973 1 477177 0 648509 1 914815 0 249048 0 144323 0 651861 1 125324 0 398102 0 514065 1 391652 1 922167 1 442795 1 226330 0 134430 0 524230 1 438817 0 293794 0 868283 0 828890 0 882920 0 918777 1 212580 0 602410 0 976971 0 630226 0 171254 0 247116 0 505969 0 653864 1 586367 0 896890 0 650026 0 547744 0 598124 0 436126 1 739447 0 427484 0 218684 0 565564 1 743163 0 604614 1 509928 0 593390 0 970607 0 174701 0 529398 1 940942 0 442677 0 365364 0 114839 1 872734 0 267885 0 740505 1 629663 0 839884 0 241562 1 405533 0 667021 1 511621 0 476923 0 735822 0 492745 0 130930 1 261119 0 280709 0 898573 0 547802 0 600845 0 390381 0 629918 0 208298 0 513099 0 184938 0 187775 1 326322 1 146138 0 336047 0 532330 1 118137 0 212674 0 935596 0 737593 1 812025 0 168151 0 594739 0 843227 0 283925 0 475588 0 751905 1 226725 1 942504 0 395572 0 889883 0 818167 0 277767 0 842618 0 577810 0 873114 0 994538 0 727792 0 522506 0 367595 0 586104 0 424862 0 512813 0 356768 1 330506 0 779075 0 799501 0 987905 1 967756 0 830414 0 127313 1 786957 0 332892 0 448642 1 526039 0 444422 0 689500 0 806081 0 384618 0 756459 0 655787 0 419954 0 275092 0 515698 1 132871 0 714929 2 297816 0 426708 1 615047 0 771236 0 235869 1 931625 0 371635 1 427199 0 261315 0 582973 0 278091 0 153154 0 515217 1 860497 0 351741 0 403737 0 162004 0 740384 0 876714 0 951543 0 576723 0 391003 0 225865 0 984948 0 890328 0 803294 0 414913 0 414519 1 818413 0 487356 0 159768 0 865839 0 406567 1 623032 1 935442 1 106873 0 563878 0 620855 0 583169 0 337677 0 445973 0 156694 0 421940 1 613226 0 804410 1 553565 1 399524 0 331595 1 380067 0 701521 0 360770 1 958785 0 797934 0 883980 0 340614 0 435784 0 563837 0 200827 0 533941 1 265026 0 354481 0 566720 0 832746 0 386690 0 979285 1 594722 0 216738 0 369048 0 514424 0 954191 0 150181 0 388671 1 457244 1 206667 1 745200 0 412703 1 736771 0 347984 0 626074 1 218109 0 999435 0 858060 0 500384 0 903785 0 873859 0 204294 0 467106 1 357713 1 890026 0 751612 1 876680 0 756981 0 121439 1 411289 0 538466 1 932097 0 463727 0 552618 1 936638 0 348814 0 944102 0 689901 0 901083 0 396224 1 682178 0 596298 0 253005 0 985924 0 631565 0 630998 0 926665 0 302669 0 620020 0 439828 1 971295 0 165565 0 936543 0 296960 1 501692 0 525224 0 355085 0 830729 0 651948 0 424358 0 131478 0 268833 1 287489 0 808153 0 687639 1 497347 0 439660 0 847123 0 172307 0 810189 0 432068 0 903203 1 253085 0 180720 0 492224 0 411477 0 107181 0 312940 0 855186 1 373935 0 812989 1 993840 0 327856 0 506333 0 263159 1 372912 0 552788 0 722747 0 248467 0 955953 0 910622 0 137675 0 343421 1 413192 0 247801 0 171147 0 431283 0 461962 0 149467 0 758740 1 628337 0 574637 0 373600 1 930032 0 396590 0 238412 1 484321 0 795847 0 218456 1 792673 0 662256 0 971338 0 714738 0 753844 0 976645 1 918037 0 996253 0 373731 0 836272 0 167231 0 743330 0 807369 0 735307 0 789208 0 585324 0 498759 1 795004 0 203250 0 430794 0 156636 0 284143 1 740518 0 445289 0 599262 0 357949 1 493161 0 320251 0 231127 1 766193 1 555374 1 491484 0 925128 0 265093 0 267808 0 116735 0 963680 0 445694 0 215534 0 232854 0 168260 0 538955 0 243226 0 246435 0 582480 1 345539 0 924318 0 726880 0 190588 0 246705 0 619589 0 164988 1 729534 0 505014 0 920826 0 534982 1 110408 0 283052 0 840806 0 382394 0 876699 0 871432 0 379882 0 852002 1 372891 0 689034 1 743092 1 599174 0 421092 0 349658 1 811042 0 505316 0 116645 0 950880 0 788502 0 627486 0 498842 0 550294 0 328387 0 540152 0 385932 0 618682 0 550930 1 998192 0 663938 0 756870 0 337158 1 919875 0 315631 0 113464 0 556415 0 250249 1 403776 0 396002 0 976908 1 509489 1 485295 0 361829 0 603632 0 783494 1 439049 0 502634 0 378588 0 794731 0 641934 0 113516 0 425631 0 542245 0 512894 1 633090 0 464234 0 290162 0 638155 0 911429 1 106186 0 311783 0 528385 1 228403 0 209177 0 497929 0 735844 0 710741 0 276804 1 507545 1 485642 1 796375 0 171183 0 110084 0 714784 1 143924 0 996850 0 284834 0 830878 1 270208 0 407958 0 832300 0 927205 0 655356 0 831053 0 432740 0 893853 0 594988 0 979544 0 191891 0 831479 1 714346 0 326289 0 944537 0 779156 0 856153 0 473338 0 521694 1 136520 0 730819 0 912665 0 469966 0 952300 1 322609 0 890280 0 431583 0 155912 0 110143 0 808544 0 409074 0 824728 1 606037 0 636843 0 111874 0 439844 0 463513 1 577858 0 607351 0 682754 0 757352 0 307469 0 526296 0 658816 0 913337 0 488464 1 480094 1 263108 0 298412 1 261905 1 674485 1 223404 0 991480 0 804219 0 483088 1 100804 1 941807 1 593466 1 437442 0 942106 0 794951 0 182450 0 730973 0 687755 0 757644 0 998865 0 944953 1 386429 1 108270 0 205134 0 749325 0 774303 0 698470 0 719989 2 309323 0 444035 1 431478 0 797634 0 284836 1 238196 1 885789 0 287436 0 496067 1 206004 0 153027 0 469426 0 654974 0 943425 0 641845 1 794534 0 357808 0 536052 0 873384 1 790225 0 587498 0 639027 0 217899 0 589094 0 458829 0 626208 0 315041 0 283267 0 442494 0 159243 0 669800 0 520179 1 607974 0 465065 1 369941 0 447226 1 831668 0 922937 0 640474 0 153298 0 334749 0 221283 0 961496 0 804751 0 369226 0 691115 0 713172 1 621756 0 615116 0 947598 0 658002 0 374545 0 805806 1 235097 0 290971 0 180286 0 662088 0 884365 0 178081 0 507452 1 990624 0 892148 1 398683 0 605100 0 143109 0 230223 1 769602 0 420815 0 973546 1 608039 0 250162 0 786432 0 445195 0 938634 0 482495 0 796005 0 910604 0 327488 0 715202 0 648852 1 516959 1 984456 1 801331 0 786103 0 684193 0 247505 0 259792 0 185124 0 760700 0 362407 0 389525 0 179538 0 265437 0 266247 0 921851 0 488724 0 192524 0 338070 0 865607 0 963285 0 728491 0 553436 0 440616 0 463237 0 753452 0 920554 0 594783 0 725330 0 607259 0 979336 1 865201 0 140977 0 787351 1 272330 1 728025 2 804608 0 718829 1 482404 0 331170 0 753056 0 910365 1 379268 0 362843 0 135400 0 798579 0 250833 0 824116 0 322613 0 871305 0 488037 0 485813 1 886473 1 907113 0 833321 0 521592 0 254837 0 634499 0 574707 0 476839 0 149601 0 630683 0 500639 0 352120 0 569245 0 907012 0 700074 0 866805 0 951863 0 211578 1 357394 0 863749 0 596914 0 684653 0 528259 1 797636 0 326180 0 620075 0 965187 0 516182 0 728839 0 771509 0 264221 0 602704 0 672416 0 545506 0 777533 0 953334 1 369781 1 990998 0 982678 0 646069 0 331683 0 364055 0 521854 0 737252 0 344480 0 898519 0 957816 0 175960 0 489618 0 717044 0 101421 0 793948 0 737483 0 695117 0 167466 0 664732 1 143038 0 979963 0 467841 0 219028 0 130156 0 762951 0 376879 1 599031 0 676255 1 985446 0 884180 1 571462 0 815883 0 258265 0 569714 0 180008 0 633375 0 556538 0 669501 1 963761 0 753005 0 454758 0 698589 0 330119 0 164464 0 927354 0 231508 0 272910 0 305758 0 950542 0 291544 0 388616 0 577992 1 342830 0 491170 0 175553 0 439341 0 221186 0 868031 0 844117 0 744557 0 159536 0 727109 0 155604 0 608443 0 117862 0 991553 0 727443 0 378587 1 420948 0 457188 0 118236 0 987524 0 490596 1 524215 0 913464 0 398484 0 752504 0 449263 0 844007 0 686522 0 670142 0 607687 0 967713 0 291902 0 149839 1 840225 0 643226 1 535879 0 746630 1 598308 1 720356 1 724752 0 148498 1 110122 0 281388 0 728600 0 231548 1 531160 2 889003 0 193213 1 557218 1 125591 1 227244 0 791425 0 354455 0 601042 0 433663 0 471938 1 564654 0 645723 0 573572 0 437960 0 649800 0 544225 0 390256 1 488597 0 133889 1 931901 1 769475 0 844062 0 844129 0 732169 0 221854 0 776950 0 291006 0 845751 1 889764 0 929306 1 515457 0 556270 0 908935 0 525862 0 490514 0 774895 0 974522 0 669809 0 182953 0 836349 1 591269 0 550127 0 663190 1 109392 0 215278 0 674570 0 681486 0 941851 0 186934 0 918516 1 533940 1 556080 0 dtype: int64
fraud_df_up2=fraud_df_up.copy()
fraud_df_up2.shape
(1000, 38)
pred = fraud_df_up2.drop('fraud_reported',axis=1)
response = fraud_df_up2['fraud_reported']
fraud_con = pd.concat([pred,response],axis=1)
from sklearn.preprocessing import OneHotEncoder,OrdinalEncoder,LabelEncoder
fraud_con["insured_sex"] = np.where(fraud_con["insured_sex"].str.contains("FEMALE"), 1, 0)
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null object 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), object(20) memory usage: 340.8+ KB
fraud_con.insured_education_level.value_counts()
JD 161 High School 160 Associate 145 MD 144 Masters 143 PhD 125 College 122 Name: insured_education_level, dtype: int64
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder()
fraud_con['insured_education_level'] = fraud_con['insured_education_level'].astype('category')
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null category 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: category(1), float64(5), int32(1), int64(12), object(19) memory usage: 334.3+ KB
fraud_con['insured_education_level'] = fraud_con['insured_education_level'].cat.codes
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null object 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(1), object(19) memory usage: 333.9+ KB
oe=OrdinalEncoder()
fraud_con.police_report_available.value_counts()
NO 343 Unknown 343 YES 314 Name: police_report_available, dtype: int64
fraud_con['police_report_available'] = fraud_con['police_report_available'].astype('category')
fraud_con['police_report_available'] = fraud_con['police_report_available'].cat.codes
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null object 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(2), object(18) memory usage: 327.1+ KB
fraud_con.auto_make.value_counts()
Dodge 80 Suburu 80 Saab 80 Nissan 78 Chevrolet 76 BMW 72 Ford 72 Toyota 70 Audi 69 Volkswagen 68 Accura 68 Jeep 67 Mercedes 65 Honda 55 Name: auto_make, dtype: int64
fraud_con.insured_education_level.value_counts()
3 161 2 160 0 145 4 144 5 143 6 125 1 122 Name: insured_education_level, dtype: int64
fraud_con['auto_make'] = fraud_con['auto_make'].astype('category')
fraud_con['auto_make'] = fraud_con['auto_make'].cat.codes
fraud_con.auto_make.value_counts()
11 80 10 80 4 80 9 78 3 76 5 72 2 72 12 70 1 69 13 68 0 68 7 67 8 65 6 55 Name: auto_make, dtype: int64
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null object 4 policy_csl 1000 non-null object 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(3), object(17) memory usage: 320.3+ KB
fraud_con.policy_state.value_counts()
OH 352 IL 338 IN 310 Name: policy_state, dtype: int64
fraud_con['policy_state'] = fraud_con['policy_state'].astype('category')
fraud_con['policy_state'] = fraud_con['policy_state'].cat.codes
fraud_con.policy_state.value_counts()
2 352 0 338 1 310 Name: policy_state, dtype: int64
fraud_con.policy_csl.value_counts()
250/500 351 100/300 349 500/1000 300 Name: policy_csl, dtype: int64
fraud_con['policy_csl'] = fraud_con['policy_csl'].astype('category')
fraud_con['policy_csl'] = fraud_con['policy_csl'].cat.codes
fraud_con.policy_csl.value_counts()
1 351 0 349 2 300 Name: policy_csl, dtype: int64
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null object 12 insured_hobbies 1000 non-null object 13 insured_relationship 1000 non-null object 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null object 18 collision_type 1000 non-null object 19 incident_severity 1000 non-null object 20 authorities_contacted 1000 non-null object 21 incident_state 1000 non-null object 22 incident_city 1000 non-null object 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null object 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null object 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null object dtypes: float64(5), int32(1), int64(12), int8(5), object(15) memory usage: 306.6+ KB
num=[]
cat=[]
for i in fraud_con.columns:
if fraud_con[i].dtype=='object':
cat.append(i)
else:
num.append(i)
cat
['policy_bind_date', 'insured_occupation', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'authorities_contacted', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'auto_model', 'fraud_reported']
fraud_con.insured_occupation.value_counts()
machine-op-inspct 93 prof-specialty 85 tech-support 78 sales 76 exec-managerial 76 craft-repair 74 transport-moving 72 other-service 71 priv-house-serv 71 armed-forces 69 adm-clerical 65 protective-serv 63 handlers-cleaners 54 farming-fishing 53 Name: insured_occupation, dtype: int64
fraud_con.insured_hobbies.value_counts()
reading 64 paintball 57 exercise 57 bungie-jumping 56 camping 55 movies 55 golf 55 kayaking 54 yachting 53 hiking 52 video-games 50 base-jumping 49 skydiving 49 board-games 48 polo 47 chess 46 dancing 43 sleeping 41 cross-fit 35 basketball 34 Name: insured_hobbies, dtype: int64
fraud_con.insured_relationship.value_counts()
own-child 183 other-relative 177 not-in-family 174 husband 170 wife 155 unmarried 141 Name: insured_relationship, dtype: int64
fraud_con.incident_severity.value_counts()
Minor Damage 354 Total Loss 280 Major Damage 276 Trivial Damage 90 Name: incident_severity, dtype: int64
fraud_con.authorities_contacted.value_counts()
Police 292 Fire 223 Other 198 Ambulance 196 None 91 Name: authorities_contacted, dtype: int64
fraud_con['authorities_contacted'] = fraud_con['authorities_contacted'].astype('category')
fraud_con['authorities_contacted'] = fraud_con['authorities_contacted'].cat.codes
fraud_con.authorities_contacted.value_counts()
4 292 1 223 3 198 0 196 2 91 Name: authorities_contacted, dtype: int64
fraud_con['insured_occupation'] = fraud_con['insured_occupation'].astype('category')
fraud_con['insured_occupation'] = fraud_con['insured_occupation'].cat.codes
fraud_con.insured_occupation.value_counts()
6 93 9 85 12 78 11 76 3 76 2 74 13 72 8 71 7 71 1 69 0 65 10 63 5 54 4 53 Name: insured_occupation, dtype: int64
num=[]
cat=[]
for i in fraud_con.columns:
if fraud_con[i].dtype=='object':
cat.append(i)
else:
num.append(i)
cat
['policy_bind_date', 'insured_hobbies', 'insured_relationship', 'incident_date', 'incident_type', 'collision_type', 'incident_severity', 'incident_state', 'incident_city', 'incident_location', 'property_damage', 'auto_model', 'fraud_reported']
fraud_con.incident_city.value_counts()
Springfield 157 Arlington 152 Columbus 149 Northbend 145 Hillsdale 141 Riverwood 134 Northbrook 122 Name: incident_city, dtype: int64
fraud_con['insured_hobbies'] = fraud_con['insured_hobbies'].astype('category')
fraud_con['insured_hobbies'] = fraud_con['insured_hobbies'].cat.codes
fraud_con.insured_hobbies.value_counts()
15 64 8 57 13 57 3 56 9 55 4 55 12 55 11 54 19 53 10 52 18 50 16 49 0 49 2 48 14 47 5 46 7 43 17 41 6 35 1 34 Name: insured_hobbies, dtype: int64
fraud_con.incident_city.value_counts()
Springfield 157 Arlington 152 Columbus 149 Northbend 145 Hillsdale 141 Riverwood 134 Northbrook 122 Name: incident_city, dtype: int64
fraud_con['incident_city'] = fraud_con['incident_city'].astype('category')
fraud_con['incident_city'] = fraud_con['incident_city'].cat.codes
fraud_con.incident_city.value_counts()
6 157 0 152 1 149 3 145 2 141 5 134 4 122 Name: incident_city, dtype: int64
fraud_con.incident_location.value_counts()
3340 3rd Hwy 1 9751 Sky Ridge 1 5765 Washington St 1 5480 3rd Ridge 1 6738 Francis Hwy 1 1388 Embaracadero Hwy 1 3492 Britain St 1 6955 Pine Drive 1 5178 Weaver Hwy 1 5812 3rd Hwy 1 9138 3rd St 1 9317 Apache Ave 1 3878 Tree Lane 1 9748 Sky Drive 1 2644 MLK Drive 1 4905 Best Lane 1 9734 2nd Ridge 1 5622 Best Ridge 1 9751 Tree St 1 7459 Flute St 1 4158 Washington Lane 1 5249 4th Ave 1 3808 5th Ave 1 6044 Weaver Drive 1 7223 Embaracadero St 1 3846 4th Hwy 1 3790 Andromedia Hwy 1 3508 Washington St 1 4390 4th Drive 1 6719 Flute St 1 2078 3rd Ave 1 6574 4th Drive 1 6681 Texas Ridge 1 4693 Lincoln Hwy 1 6494 4th Ave 1 1269 Flute Drive 1 7574 4th St 1 8879 1st Drive 1 3097 4th Drive 1 6110 Rock Ridge 1 4985 Sky Lane 1 4477 5th Ave 1 2293 Washington Ave 1 4782 Sky Lane 1 7717 Britain Hwy 1 4937 Flute Drive 1 1941 5th Ridge 1 8085 Andromedia St 1 5914 Oak Ave 1 5074 3rd St 1 5383 Maple Drive 1 4053 Sky Lane 1 6853 Sky Hwy 1 5007 Oak St 1 9875 MLK Ave 1 4910 1st Lane 1 7609 Rock St 1 1215 Pine Hwy 1 7299 Apache St 1 7877 Sky Lane 1 9562 4th Ridge 1 3706 4th Hwy 1 3098 Oak Lane 1 7819 2nd Ave 1 6067 Weaver Ridge 1 4687 5th Drive 1 3323 1st Lane 1 5160 2nd Hwy 1 4931 Maple Drive 1 7393 Washington St 1 7502 Rock Lane 1 9397 Francis St 1 1923 2nd Hwy 1 3814 Britain Drive 1 3100 Best St 1 1929 Britain Drive 1 3966 Oak Hwy 1 7041 Tree Ridge 1 8973 Washington St 1 5455 Oak Hwy 1 8995 1st Ave 1 5812 Oak St 1 7069 4th Hwy 1 2577 Washington Drive 1 2494 Andromedia Drive 1 8049 4th St 1 1681 Cherokee Hwy 1 4254 Best Ridge 1 5782 Rock Drive 1 7447 Lincoln Ridge 1 4983 MLK Ridge 1 3289 Britain Drive 1 9358 Texas Ridge 1 5260 Francis Drive 1 4981 Flute Hwy 1 9910 Maple Ave 1 1512 Rock Lane 1 3422 Flute St 1 1643 Washington Hwy 1 5621 4th Ave 1 2509 Rock Drive 1 5586 2nd St 1 6634 Texas Ridge 1 9369 Flute Hwy 1 3423 Francis Ave 1 3172 Tree Ridge 1 1030 Pine Lane 1 7142 5th Lane 1 1832 Elm Hwy 1 5093 Flute Lane 1 3171 Andromedia Lane 1 7178 Best Drive 1 8150 Washington Ridge 1 5540 Sky St 1 5585 Washington Drive 1 7121 Rock St 1 1879 4th Lane 1 7066 Texas Ave 1 2886 Tree Ridge 1 9798 Sky Ridge 1 1589 Best Ave 1 3659 Oak Lane 1 5058 4th Lane 1 4095 MLK St 1 1536 Flute Drive 1 3327 Lincoln Drive 1 3275 Pine St 1 6428 Andromedia Lane 1 2711 Britain Ave 1 9573 2nd Ave 1 3439 Andromedia Hwy 1 7909 Andromedia Hwy 1 7684 Francis Ridge 1 8509 Apache St 1 4554 Sky Ave 1 6435 Texas Ave 1 8845 5th Ave 1 3288 Tree Lane 1 8926 Texas Ridge 1 3799 Embaracadero Drive 1 2756 Britain Hwy 1 5874 1st Hwy 1 7331 Sky Hwy 1 8821 Elm St 1 9422 Washington Ridge 1 2986 MLK Drive 1 9633 Rock Hwy 1 2651 MLK Lane 1 3263 Pine Ridge 1 6583 MLK Ridge 1 6608 Apache Lane 1 1699 Oak Drive 1 6315 2nd Lane 1 1558 1st Ridge 1 1012 5th Lane 1 9325 Lincoln Drive 1 7733 Britain Lane 1 5191 4th St 1 4585 Francis Ave 1 4176 Britain Hwy 1 3847 Elm Hwy 1 2832 Andromedia Lane 1 6931 Elm St 1 4755 1st St 1 2199 Texas Drive 1 7976 Britain Drive 1 8489 Pine Hwy 1 8983 Francis Ridge 1 4876 Washington Drive 1 7705 Lincoln Drive 1 6516 Solo Drive 1 4214 MLK Ridge 1 4231 3rd Ave 1 1738 Solo Lane 1 5743 4th Ridge 1 8212 Rock Ave 1 2823 Weaver Lane 1 3052 Weaver Ridge 1 7783 Lincoln Hwy 1 6515 Oak Lane 1 5779 2nd Lane 1 8021 Flute Ave 1 3414 Elm Ave 1 3995 Lincoln Hwy 1 9322 Rock Hwy 1 8954 Apache Lane 1 3102 Apache St 1 3903 Oak Ave 1 1220 MLK Ave 1 7316 Texas Ave 1 5985 Lincoln Lane 1 6128 Elm Lane 1 9153 3rd Hwy 1 8862 Maple Ridge 1 8081 Flute Ridge 1 4098 Weaver Ridge 1 3089 Oak Ridge 1 5771 Sky Ave 1 6603 Francis Hwy 1 8920 Best Ave 1 8492 Weaver Hwy 1 4264 Lincoln Ridge 1 4558 3rd Hwy 1 1992 Britain Drive 1 1762 Maple Hwy 1 4279 Solo Drive 1 4431 Rock St 1 5839 Weaver Lane 1 2900 Sky Drive 1 2914 Oak Drive 1 1589 Pine St 1 1880 Weaver Drive 1 3900 Texas St 1 4793 4th Ridge 1 9154 MLK Hwy 1 3104 Sky Drive 1 6045 Andromedia St 1 1628 Best Drive 1 7002 Oak Hwy 1 9489 3rd St 1 4188 Britain Ave 1 3246 Britain Ridge 1 7780 Flute Lane 1 4577 Sky Hwy 1 2430 MLK Ave 1 7281 Maple Hwy 1 8655 Cherokee Lane 1 5584 Britain Lane 1 8964 Francis St 1 8188 Tree Ave 1 6303 1st Drive 1 3693 Pine Ave 1 9878 Washington Ave 1 6011 Britain St 1 6456 Andromedia Drive 1 2311 4th St 1 8215 Flute Drive 1 8639 5th Hwy 1 3751 Tree Hwy 1 6724 Andromedia St 1 7756 Pine Hwy 1 3486 Flute Ave 1 4780 Best Drive 1 5431 3rd Ridge 1 7740 MLK St 1 5459 MLK Ave 1 1240 Tree Lane 1 5051 Elm St 1 5640 Embaracadero Lane 1 5380 Pine St 1 7816 MLK Lane 1 8524 Pine Lane 1 9529 4th Drive 1 8897 Sky St 1 7504 Flute Drive 1 9293 Pine Lane 1 3550 Washington Ave 1 2753 Cherokee Ave 1 7155 Apache Drive 1 4939 Oak Lane 1 2980 Sky Ridge 1 7551 Britain Lane 1 3805 Lincoln Hwy 1 2787 MLK St 1 6165 Rock Ridge 1 7135 Flute Lane 1 3167 2nd St 1 6536 MLK Hwy 1 1540 Apache Lane 1 7835 Cherokee Hwy 1 3094 Best Lane 1 3982 Weaver Lane 1 4995 Weaver Ridge 1 9980 Lincoln Ave 1 3590 Best Hwy 1 3443 Maple Ridge 1 2809 Francis Lane 1 2063 Weaver St 1 5455 Tree Ridge 1 7405 Oak St 1 7615 Weaver Drive 1 8811 Maple Hwy 1 2306 5th Lane 1 2014 Rock Ave 1 9066 Best Ridge 1 1818 Tree St 1 5868 Sky Hwy 1 8064 4th Ave 1 3555 Francis Ridge 1 9611 Pine Ridge 1 4671 5th Ridge 1 6329 Apache Ave 1 9929 Rock Drive 1 4234 Cherokee Lane 1 2646 MLK Drive 1 9484 Pine Drive 1 7500 Texas Ridge 1 3177 MLK Ridge 1 4116 Embaracadero Lane 1 7162 Maple Ave 1 8749 Tree St 1 7570 Cherokee Drive 1 4496 Pine Lane 1 3639 Flute Hwy 1 1028 Sky Lane 1 1454 5th Ridge 1 8957 Weaver Drive 1 1087 Flute Drive 1 6484 Tree Drive 1 3447 Solo Ave 1 5924 Maple Drive 1 5499 Flute Ridge 1 9794 Embaracadero St 1 6260 5th Lane 1 1687 3rd Lane 1 5022 1st St 1 5855 Apache St 1 3872 5th Drive 1 7819 Oak St 1 7426 Rock Drive 1 7112 Weaver Ave 1 2117 Lincoln Hwy 1 1135 Solo Lane 1 5053 Tree Drive 1 5506 Best St 1 2696 Cherokee Ridge 1 6770 1st St 1 4905 Francis Ave 1 3706 Texas Hwy 1 9879 Apache Drive 1 5483 Francis Drive 1 2048 3rd Ridge 1 2733 Texas Drive 1 8233 Tree Drive 1 2215 Best Ave 1 2942 1st Lane 1 4965 MLK Drive 1 5124 Maple St 1 1598 3rd Drive 1 5667 4th Drive 1 7785 Lincoln Lane 1 2352 MLK Drive 1 8991 Embaracadero Ave 1 2201 4th Lane 1 4652 Flute Drive 1 4058 Tree Drive 1 9109 Britain Drive 1 8442 Britain Hwy 1 7705 Best Ridge 1 2850 Washington St 1 5445 Tree Hwy 1 1126 Texas Hwy 1 7534 MLK Hwy 1 8872 Oak Ridge 1 1515 Pine Lane 1 9169 Pine Ridge 1 6259 Lincoln Hwy 1 8946 2nd Drive 1 5363 Weaver Lane 1 5224 5th Lane 1 4272 Oak Ridge 1 1422 Flute Ave 1 7828 Cherokee Ave 1 3726 MLK Hwy 1 1617 Rock Drive 1 3110 Lincoln Lane 1 9279 Oak Hwy 1 3835 5th Ave 1 4434 Lincoln Ave 1 3654 Cherokee Ave 1 9169 Cherokee Hwy 1 6191 Oak Lane 1 3282 4th Lane 1 2878 Britain Hwy 1 5236 Weaver Drive 1 5650 Rock Ave 1 9043 Maple Hwy 1 9101 2nd Hwy 1 8770 1st Lane 1 1957 Washington Ave 1 6608 MLK Hwy 1 7121 Francis Lane 1 6357 Texas Lane 1 4615 Embaracadero Ave 1 7552 3rd St 1 4296 Pine Hwy 1 1218 Sky Hwy 1 3769 Sky St 1 7954 Tree Ridge 1 5862 Apache Ridge 1 3771 4th St 1 1578 5th Lane 1 1985 5th Ave 1 8097 Maple Lane 1 7428 Sky Hwy 1 8602 Washington Ridge 1 8353 Britain Ridge 1 3122 Apache Drive 1 6638 Tree Drive 1 8125 Texas Ridge 1 4414 Solo Drive 1 3777 Maple Ave 1 4268 2nd Ave 1 1229 5th Ave 1 4545 4th Ridge 1 7238 2nd St 1 6660 MLK Drive 1 5352 Lincoln Drive 1 7701 Tree St 1 1707 Sky Ave 1 2087 Apache Ave 1 7144 Andromedia St 1 6390 Apache St 1 4539 Texas St 1 5865 Sky Lane 1 4055 2nd Drive 1 2849 Pine Drive 1 2889 Weaver St 1 5277 Texas Lane 1 8668 Flute St 1 8618 Texas Lane 1 3930 Embaracadero St 1 2905 Embaracadero Drive 1 9236 2nd Hwy 1 6443 Washington Ridge 1 9603 Texas Lane 1 6717 Best Drive 1 8167 Apache Ave 1 1376 Pine St 1 6731 Andromedia Hwy 1 4721 Cherokee Hwy 1 2253 Maple Ave 1 9918 Andromedia Drive 1 5276 2nd Lane 1 9007 Francis Hwy 1 8245 4th Hwy 1 3227 Maple Ave 1 1306 Andromedia St 1 5901 Elm Drive 1 6429 4th Hwy 1 9081 Cherokee Hwy 1 7535 5th Lane 1 8477 Francis Hwy 1 7201 Washington Ave 1 2230 1st St 1 9610 Cherokee St 1 3743 Andromedia Ridge 1 6479 Francis Ave 1 3418 Texas Lane 1 2086 Francis Drive 1 7197 2nd Drive 1 8459 Apache Ave 1 9760 4th Hwy 1 4453 Best Ave 1 4291 Sky Hwy 1 9078 Francis Ridge 1 2003 2nd Hwy 1 8586 1st Ridge 1 1091 1st Drive 1 3053 Lincoln Drive 1 9148 4th Hwy 1 2100 MLK St 1 8809 Flute St 1 1620 Oak Ave 1 7529 Solo Ridge 1 5269 Flute Hwy 1 2337 Lincoln Hwy 1 2289 Weaver Ridge 1 8579 Apache Drive 1 6331 MLK Ave 1 7629 5th St 1 6702 Andromedia St 1 1381 Francis Ave 1 2299 Britain Drive 1 6945 Texas Hwy 1 8718 Apache Lane 1 3658 Rock Drive 1 6035 Rock Ave 1 7476 4th St 1 3041 3rd Ave 1 9245 Weaver Ridge 1 4107 MLK Ridge 1 6522 Apache Drive 1 6117 4th Ave 1 7923 Elm Ave 1 1741 Best Ridge 1 1806 Weaver Ridge 1 3488 Flute Lane 1 2539 Embaracadero Ridge 1 1133 Apache St 1 1654 Pine St 1 5769 Texas Lane 1 8704 Britain Lane 1 5868 Best Drive 1 6104 Oak Ave 1 2668 Cherokee St 1 9488 Best Drive 1 9856 Apache St 1 4369 Maple Lane 1 7582 Pine Drive 1 2903 Weaver Drive 1 6848 Elm Hwy 1 5279 Pine Ridge 1 8492 Andromedia Ridge 1 6957 Weaver Drive 1 3618 Sky Ave 1 6408 Weaver Ridge 1 7630 Rock Drive 1 7253 MLK St 1 4866 4th Hwy 1 3770 Flute Drive 1 6981 Weaver St 1 4492 Andromedia Ave 1 6440 Rock Lane 1 1353 Washington St 1 4486 Cherokee Ridge 1 7575 Pine St 1 3220 Rock Drive 1 2725 Britain Ridge 1 6409 Cherokee Drive 1 7649 Texas St 1 9818 Cherokee Ave 1 8538 Texas Lane 1 6206 3rd Ridge 1 3184 Oak Ave 1 9685 Sky Ridge 1 5771 Best St 1 7434 Oak Hwy 1 7644 Tree Ridge 1 7721 Washington Ridge 1 2526 Embaracadero Ave 1 7825 1st Ridge 1 7098 Lincoln Hwy 1 7900 Sky Hwy 1 4699 Texas Ridge 1 8080 Oak Lane 1 9214 Elm Ridge 1 8214 Flute St 1 1679 2nd Hwy 1 2272 Embaracadero Drive 1 1328 Texas Lane 1 2275 Best Lane 1 8100 3rd Ave 1 3567 4th Drive 1 3998 Flute St 1 3540 Maple St 1 6309 Cherokee Ave 1 3311 2nd Drive 1 8336 1st Ridge 1 3796 Cherokee Drive 1 6550 Andromedia St 1 6751 5th Hwy 1 8983 Tree St 1 3416 Washington Drive 1 9038 2nd Lane 1 3087 Oak Hwy 1 7756 Solo Drive 1 1824 5th Lane 1 1316 Britain Ridge 1 7583 Washington Ave 1 1267 Francis Hwy 1 7061 Cherokee Drive 1 1102 Apache Hwy 1 2862 Tree Ridge 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 2457 Washington Ave 1 7773 Tree Hwy 1 9373 Pine Hwy 1 3492 Flute Lane 1 7082 Oak Ridge 1 8822 Sky St 1 9911 Britain Lane 1 9728 Britain Hwy 1 6058 Andromedia Hwy 1 4460 4th Lane 1 5719 2nd Lane 1 9730 2nd Hwy 1 6317 Best St 1 6985 Maple Lane 1 6467 Best Ave 1 6838 Flute Lane 1 1810 Elm Hwy 1 2500 Tree St 1 6971 Best Ridge 1 6859 Flute Ridge 1 8404 Embaracadero St 1 9633 4th St 1 2469 Francis Lane 1 2492 Lincoln Lane 1 1830 Sky St 1 2220 1st Lane 1 6939 3rd Hwy 1 7709 Rock Lane 1 9633 MLK Lane 1 4538 Flute Hwy 1 6723 Best Drive 1 1248 MLK Ridge 1 3982 Washington Hwy 1 5806 Embaracadero St 1 9067 Texas Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 6619 Flute Ave 1 7295 Tree Hwy 1 3320 5th Hwy 1 2603 Andromedia Hwy 1 6874 Maple Ridge 1 4862 Lincoln Hwy 1 3419 Apache St 1 5608 Solo St 1 9240 Britain Ave 1 1416 Cherokee Ridge 1 4672 MLK St 1 9821 Francis Ave 1 2193 4th Ridge 1 8689 Maple Hwy 1 7859 4th Ridge 1 7928 Maple Ridge 1 5211 Weaver Drive 1 4907 Andromedia Drive 1 5639 1st Ridge 1 9706 MLK Lane 1 1437 3rd Lane 1 9177 Texas Ave 1 4633 5th Lane 1 2324 Texas Ridge 1 9720 Lincoln Hwy 1 3495 Britain Drive 1 6359 MLK Ridge 1 1331 Elm Ridge 1 6655 5th Drive 1 9988 Rock Ridge 1 6582 Elm Lane 1 2290 4th Ave 1 1529 Elm Ridge 1 5678 Lincoln Drive 1 9397 5th Hwy 1 1562 Britain St 1 9439 MLK St 1 7240 5th Ridge 1 5790 Flute Ridge 1 2873 Flute Ave 1 3592 MLK Ridge 1 3221 Solo Ridge 1 4239 Weaver Ave 1 7523 Oak Lane 1 2889 Francis St 1 9523 Solo Hwy 1 6375 2nd Lane 1 7314 Tree Drive 1 9278 Francis Ridge 1 5201 Texas Hwy 1 7601 Andromedia Lane 1 8617 Best Ave 1 2644 Elm Drive 1 2774 Apache Drive 1 2100 Francis Drive 1 3457 Texas Lane 1 9103 MLK Lane 1 1507 Solo Ave 1 4653 Pine St 1 6684 Solo Lane 1 9580 MLK Ave 1 3006 Lincoln Ridge 1 1358 Maple St 1 3029 5th Ave 1 2217 Tree Lane 1 4335 1st St 1 5189 Francis Drive 1 1553 Lincoln St 1 3660 Andromedia Hwy 1 5812 Weaver Ave 1 5499 Elm Hwy 1 4567 Pine Ave 1 7511 1st Ave 1 4710 Lincoln Hwy 1 5778 Pine Ridge 1 4143 Maple Ridge 1 4175 Elm Ridge 1 8281 Lincoln Lane 1 1725 Solo Lane 1 3929 Elm Ave 1 9787 Andromedia Ave 1 5894 Flute Drive 1 1298 Maple Hwy 1 4972 Francis Lane 1 2123 MLK Ridge 1 2204 Washington Lane 1 5071 Flute Ridge 1 1833 Solo Ave 1 4232 Britain Ridge 1 5783 Oak Ave 1 4939 Best St 1 6956 Maple Drive 1 2968 Andromedia Ave 1 1364 Best St 1 6618 Cherokee Drive 1 6092 5th Ave 1 5474 Weaver Hwy 1 1580 Maple Lane 1 8269 Sky Hwy 1 4546 Tree St 1 1173 Andromedia Ave 1 4964 Elm Lane 1 5994 5th Ave 1 8014 Embaracadero Drive 1 4154 Lincoln Hwy 1 3966 Francis Ridge 1 5838 Pine Lane 1 6678 Weaver Drive 1 3884 Pine Lane 1 8211 Sky Hwy 1 9418 5th Hwy 1 5071 1st Lane 1 6378 Britain Ave 1 5971 5th Hwy 1 4814 Lincoln Lane 1 6149 Best Ridge 1 9214 Texas Drive 1 4642 Rock Ridge 1 9239 Washington Ridge 1 7846 Andromedia Drive 1 9760 Solo Lane 1 2577 Texas Ridge 1 8876 1st St 1 5028 Maple Ridge 1 9105 Tree Lane 1 2654 Embaracadero St 1 6834 1st Drive 1 3553 Texas Ave 1 6399 Oak Drive 1 6179 3rd Ridge 1 5821 2nd St 1 7628 4th Lane 1 7488 Lincoln Lane 1 4857 Weaver St 1 2920 5th Ave 1 8118 Elm Ridge 1 8576 Andromedia St 1 5104 Francis Drive 1 3929 Oak Drive 1 1989 Solo Lane 1 8864 Tree Ridge 1 6250 1st Ridge 1 2654 Elm Drive 1 1655 Francis Hwy 1 3926 Rock Lane 1 6158 Sky Ridge 1 8917 Tree Ridge 1 4981 Weaver St 1 2950 MLK Ave 1 3127 Flute St 1 1346 5th Lane 1 1532 Washington St 1 6501 5th Drive 1 8940 Elm Ave 1 5649 Texas Ave 1 9719 4th Lane 1 1273 Rock Lane 1 3842 Solo Ridge 1 8766 Lincoln Lane 1 7236 Apache Lane 1 7544 Washington Ave 1 8096 Apache Hwy 1 3525 3rd Hwy 1 4826 5th St 1 9224 Sky Drive 1 4020 Best Drive 1 3809 Texas Lane 1 7495 Washington Ave 1 9020 Elm Ave 1 2808 Elm St 1 6492 4th Lane 1 2003 Maple Hwy 1 3618 Maple Lane 1 9657 5th Ave 1 8805 Cherokee Drive 1 3061 Francis Hwy 1 8078 Britain Hwy 1 8621 Best Ridge 1 2757 4th Hwy 1 6888 Elm Ridge 1 1386 Britain St 1 5969 Francis St 1 7168 Andromedia Ridge 1 1472 4th Drive 1 6676 Tree Lane 1 8941 Solo Ridge 1 1186 Rock St 1 2537 5th Ave 1 2804 Best St 1 6581 Rock Ridge 1 1275 4th Ridge 1 9942 Tree Ave 1 3196 Cherokee St 1 2318 Washington Hwy 1 3376 5th Drive 1 6851 3rd Drive 1 2865 Maple Lane 1 4347 2nd Ridge 1 9682 Cherokee Ridge 1 1213 4th Lane 1 9352 Washington Ave 1 6256 Elm St 1 5226 Maple St 1 1910 Sky Ave 1 1815 Cherokee Drive 1 4574 Britain Hwy 1 2280 4th Ave 1 7397 4th Drive 1 6605 Tree Ave 1 9573 Weaver Ave 1 8101 3rd Ridge 1 5532 Weaver Ridge 1 9816 Britain St 1 8204 Pine Lane 1 4627 Elm Ridge 1 3092 Texas Drive 1 1914 Francis St 1 5925 Tree Hwy 1 3397 5th Ave 1 7281 Oak St 1 4447 Francis Hwy 1 1845 Best St 1 1325 1st Lane 1 2697 Oak Drive 1 7121 Britain Drive 1 7477 MLK Drive 1 8542 Lincoln Ridge 1 5061 Francis Ave 1 2777 Solo Drive 1 6493 Lincoln Lane 1 1953 Sky Lane 1 1618 Maple Hwy 1 1371 Texas Lane 1 6193 1st Hwy 1 3653 Elm Drive 1 4429 Washington St 1 3707 Oak Ridge 1 3664 Francis Ridge 1 8667 Weaver Lane 1 5280 Pine Ave 1 1128 Maple Lane 1 7693 Britain Lane 1 6741 Oak Ridge 1 7460 Apache Lane 1 5318 5th Ave 1 8638 3rd Ave 1 7693 Cherokee Lane 1 3066 Francis Ave 1 4835 Britain Ridge 1 4629 Elm Ridge 1 1705 Weaver St 1 6309 5th Ave 1 2397 Cherokee Ave 1 6658 Weaver St 1 9742 5th Ridge 1 9034 Weaver Ridge 1 8381 Solo Hwy 1 9316 Pine Ave 1 5630 1st Drive 1 5997 Embaracadero Drive 1 8832 Pine Drive 1 6677 Andromedia Drive 1 4994 Lincoln Drive 1 9847 Elm St 1 5602 Britain St 1 1809 Sky St 1 1555 Washington Lane 1 1916 Elm St 1 7782 Rock St 1 5532 Francis Lane 1 7745 Washington Ridge 1 9724 Maple St 1 8306 1st Drive 1 4434 Weaver St 1 3039 Oak Hwy 1 7897 Lincoln St 1 8456 1st Ave 1 6706 Francis Drive 1 1320 Flute Lane 1 1840 Embaracadero Ave 1 9138 1st St 1 8212 Flute Ridge 1 8548 Cherokee Ridge 1 7791 Britain Ridge 1 9935 4th Drive 1 6355 4th Hwy 1 9588 Solo St 1 6068 2nd St 1 3167 4th Ridge 1 5333 MLK Lane 1 3028 5th St 1 8493 Apache Drive 1 9070 Tree Ave 1 1956 Apache St 1 8198 Embaracadero Lane 1 1331 Britain Hwy 1 8782 3rd St 1 9737 Solo Hwy 1 5418 Britain Ave 1 5663 Oak Lane 1 3841 Washington Lane 1 5780 4th Ave 1 6934 Lincoln Ave 1 6738 Washington Hwy 1 7877 3rd Ridge 1 7576 Pine Ridge 1 9744 Texas Drive 1 1110 4th Drive 1 5312 Francis Ridge 1 6530 Weaver Ave 1 4955 Lincoln Ridge 1 1365 Francis Ave 1 4237 4th St 1 6604 Apache Drive 1 7797 Tree Ridge 1 8758 5th St 1 8907 Tree Ave 1 2798 1st Ave 1 9417 Tree Hwy 1 2333 Maple Lane 1 4618 Flute Ave 1 8906 Elm Lane 1 4755 Best Lane 1 5846 Weaver Drive 1 2005 Texas Hwy 1 6259 Weaver St 1 1491 Francis Ridge 1 2381 1st Hwy 1 4614 MLK Ave 1 9360 3rd Drive 1 2037 5th Drive 1 2352 Sky Drive 1 8742 4th St 1 2533 Elm St 1 3915 Embaracadero St 1 3834 Pine St 1 3753 Francis Lane 1 6137 MLK St 1 8949 Rock Hwy 1 4475 Lincoln Ridge 1 1546 Cherokee Ave 1 7571 Elm Ridge 1 5168 5th Ave 1 7973 4th St 1 4119 Texas St 1 3797 Solo Lane 1 4885 Oak Lane 1 1919 4th Lane 1 9082 3rd Lane 1 1515 Embaracadero St 1 8701 5th Lane 1 7042 Maple Ridge 1 3952 Andromedia Lane 1 3925 Sky St 1 2299 1st St 1 6751 Pine Ridge 1 8203 Lincoln Ave 1 3697 Apache Drive 1 3818 Texas Ridge 1 1123 5th Lane 1 4242 Rock Lane 1 4702 Texas Drive 1 3656 Solo Ave 1 9286 Oak Ave 1 8453 Elm St 1 2123 Texas Ave 1 7327 Lincoln Drive 1 4519 Embaracadero St 1 5475 Rock Lane 1 6889 Cherokee St 1 7466 MLK Ridge 1 4872 Rock Ridge 1 8834 Elm Drive 1 5341 5th Ave 1 5904 1st Drive 1 2376 Sky Ridge 1 7108 Tree St 1 7380 5th Hwy 1 3936 Tree Drive 1 6451 1st Hwy 1 5650 Sky Drive 1 6668 Andromedia Ridge 1 4625 MLK Drive 1 1469 Lincoln Drive 1 3998 4th Hwy 1 1951 Best Ave 1 8368 Cherokee Ave 1 8624 Francis Ave 1 4394 Oak St 1 8991 Texas Hwy 1 7930 Texas Ave 1 2617 Andromedia Drive 1 8006 Maple Hwy 1 9639 Britain Ridge 1 2820 Britain St 1 6384 5th Ridge 1 8917 Cherokee Lane 1 Name: incident_location, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 06-09-2005 1 30-04-2002 1 19-05-2014 1 21-05-2010 1 09-08-1993 1 12-10-2002 1 20-06-2002 1 28-04-2013 1 15-04-2013 1 01-03-2012 1 19-01-1998 1 28-07-2008 1 04-12-1995 1 20-02-2009 1 28-04-1994 1 24-05-2001 1 19-09-1991 1 20-07-1990 1 13-06-1994 1 10-01-2012 1 08-03-1995 1 10-02-1998 1 19-07-2006 1 11-01-2009 1 12-01-2012 1 03-09-2008 1 11-07-1991 1 29-08-2010 1 09-06-2001 1 05-07-2007 1 06-06-2002 1 05-07-2000 1 26-11-2001 1 05-02-2006 1 27-04-2009 1 08-07-2001 1 05-06-2011 1 23-04-2008 1 11-08-2007 1 15-11-2000 1 17-02-2010 1 01-03-2002 1 03-05-1991 1 17-11-2009 1 05-05-1998 1 10-12-2014 1 13-06-1992 1 29-05-1999 1 05-12-2014 1 25-04-2014 1 03-12-2005 1 05-12-1995 1 21-02-2006 1 27-10-2013 1 16-10-2002 1 12-10-1994 1 20-02-1998 1 05-02-1991 1 18-10-2005 1 18-11-2011 1 27-02-1994 1 22-03-2000 1 08-01-1994 1 08-06-1994 1 18-12-1994 1 19-03-1992 1 29-08-1995 1 06-06-2001 1 12-01-2010 1 29-03-2004 1 13-09-2003 1 08-04-1991 1 26-03-1995 1 15-07-1990 1 12-11-2009 1 01-02-1990 1 11-05-1996 1 23-06-2000 1 17-06-2009 1 24-04-1995 1 27-09-1990 1 03-03-1997 1 05-11-2008 1 02-09-2014 1 21-04-2003 1 03-11-1996 1 17-08-2014 1 15-09-1990 1 31-10-1997 1 03-10-1994 1 30-12-2002 1 06-03-2005 1 20-05-1995 1 16-07-1998 1 11-12-1992 1 09-04-1999 1 19-03-2014 1 15-12-1996 1 25-01-1999 1 08-02-1991 1 05-09-1996 1 10-11-2005 1 10-06-2014 1 18-11-1994 1 05-02-1997 1 30-08-2014 1 25-11-2009 1 12-09-1994 1 23-10-1996 1 13-11-2002 1 19-05-1990 1 19-04-2002 1 14-02-1994 1 16-09-1990 1 18-08-1990 1 15-02-1990 1 18-10-2012 1 18-06-2011 1 05-01-2012 1 23-08-2003 1 22-12-1995 1 09-12-1992 1 14-03-1995 1 21-04-1997 1 09-04-2003 1 15-03-2007 1 17-12-1995 1 03-12-2012 1 12-08-1999 1 15-08-1990 1 08-04-1996 1 18-12-1996 1 17-09-1994 1 04-06-2012 1 30-01-1992 1 18-02-1997 1 12-12-2011 1 23-08-1994 1 17-07-2005 1 10-03-1997 1 31-05-2004 1 25-02-2011 1 09-07-2001 1 01-08-2003 1 07-02-2007 1 05-04-2002 1 10-12-2005 1 18-06-1993 1 08-08-2010 1 18-11-1993 1 24-04-2013 1 28-04-2007 1 29-11-2009 1 11-06-2014 1 26-03-1990 1 20-06-2012 1 28-06-2010 1 16-07-1995 1 29-11-2004 1 29-09-2005 1 21-04-2010 1 28-03-1990 1 09-02-1993 1 04-04-2003 1 14-11-2014 1 08-02-2009 1 29-10-1991 1 01-02-1998 1 22-06-2009 1 09-05-2009 1 14-01-2005 1 21-04-2014 1 15-07-2000 1 30-09-1991 1 02-01-2004 1 04-12-2007 1 10-12-1993 1 11-05-1997 1 14-10-1992 1 02-10-2003 1 11-01-2010 1 25-01-2006 1 25-06-2002 1 20-11-2002 1 21-08-1991 1 03-06-2014 1 18-12-2013 1 12-10-1998 1 12-07-2013 1 29-06-2006 1 17-06-2005 1 05-10-1999 1 09-04-2001 1 13-10-1990 1 13-04-2006 1 02-06-2012 1 06-09-1995 1 11-09-1997 1 26-08-2004 1 08-12-2001 1 24-10-2004 1 24-09-1994 1 25-11-1994 1 01-10-2010 1 17-02-2003 1 16-12-2007 1 22-06-1990 1 10-02-1993 1 30-07-2003 1 17-07-1995 1 17-08-1994 1 27-09-2010 1 08-05-2001 1 23-05-1999 1 17-02-1995 1 13-09-2002 1 30-06-2004 1 04-04-1992 1 16-11-2004 1 18-07-2002 1 09-03-2007 1 08-01-1990 1 02-09-1996 1 06-05-2002 1 12-03-1994 1 11-12-1994 1 10-10-1993 1 10-11-1992 1 20-01-2014 1 16-09-1997 1 13-11-2014 1 12-04-2013 1 05-08-1993 1 06-08-1999 1 14-05-2006 1 28-10-1999 1 11-02-2003 1 17-09-2014 1 02-12-2011 1 25-02-2008 1 08-08-1994 1 25-05-2002 1 25-04-1991 1 06-05-1991 1 04-01-1996 1 18-02-2000 1 21-12-2006 1 22-05-1999 1 14-06-2004 1 18-10-1996 1 04-12-2005 1 27-07-2005 1 23-12-2013 1 04-05-1995 1 14-11-1999 1 29-05-2003 1 13-04-2002 1 02-10-1990 1 05-08-2012 1 28-04-2005 1 20-07-2008 1 15-03-1992 1 03-11-1991 1 18-03-2008 1 24-09-1992 1 01-12-2008 1 09-08-2002 1 09-02-2000 1 18-02-1995 1 07-06-1994 1 16-06-1993 1 05-07-2009 1 10-10-2005 1 15-06-2004 1 25-12-2001 1 05-08-2014 1 07-04-1994 1 30-12-2011 1 18-06-1998 1 10-09-1998 1 25-07-2011 1 03-10-2000 1 18-02-1999 1 25-08-2011 1 26-06-2001 1 16-03-2008 1 14-01-2008 1 03-02-1990 1 21-07-2008 1 10-04-1992 1 07-09-2006 1 09-08-2003 1 21-10-2009 1 23-07-2009 1 04-01-2009 1 18-08-1996 1 03-09-2013 1 04-02-1992 1 19-12-1998 1 08-08-2013 1 05-12-2013 1 14-01-1998 1 01-02-2011 1 07-11-2008 1 29-05-1995 1 15-11-2004 1 09-10-2012 1 26-01-1996 1 20-10-1993 1 25-06-2011 1 24-03-2007 1 22-11-2012 1 16-11-2000 1 17-08-1991 1 06-10-2012 1 22-03-1992 1 12-04-2002 1 07-04-2005 1 02-06-2002 1 21-05-2000 1 11-07-2007 1 17-02-2009 1 30-04-2007 1 19-12-1997 1 17-03-1990 1 11-11-1994 1 07-09-1999 1 16-02-2001 1 28-10-2008 1 30-03-1995 1 15-05-2000 1 23-01-1996 1 23-02-1990 1 20-08-1990 1 06-06-1993 1 18-02-2007 1 11-11-1996 1 03-07-1992 1 10-07-2012 1 03-03-2007 1 15-06-2014 1 27-06-2005 1 05-12-2009 1 08-01-2013 1 27-05-2003 1 21-02-1995 1 11-04-2005 1 23-01-2002 1 18-01-2003 1 29-01-1993 1 07-03-2008 1 26-01-1994 1 14-03-1990 1 19-04-2009 1 01-07-2005 1 08-07-1996 1 16-06-2003 1 20-09-1993 1 28-05-2014 1 19-04-2006 1 30-11-1996 1 25-05-2011 1 15-10-1996 1 21-03-1998 1 10-01-2004 1 09-01-1999 1 28-10-1997 1 28-03-2001 1 13-07-2013 1 09-12-2001 1 05-08-1997 1 03-11-2009 1 21-11-1991 1 06-03-2004 1 21-01-2002 1 02-08-1990 1 14-02-1996 1 28-01-2003 1 29-04-1990 1 12-10-2006 1 28-03-2003 1 11-03-2007 1 02-10-1992 1 04-11-1991 1 29-03-1995 1 27-06-2006 1 17-08-2011 1 22-01-2011 1 04-01-2003 1 24-05-2006 1 30-10-1997 1 14-10-2005 1 01-05-1999 1 19-06-1996 1 15-08-2000 1 07-04-1992 1 26-05-2002 1 05-05-1993 1 27-05-1994 1 22-11-2008 1 24-12-2006 1 05-08-2006 1 27-05-2002 1 30-04-2006 1 18-03-2003 1 27-11-1990 1 24-04-2012 1 21-12-1999 1 06-06-1992 1 06-05-2014 1 03-03-1992 1 12-09-2001 1 06-06-2011 1 15-07-2004 1 15-01-1992 1 21-12-1996 1 03-08-2010 1 07-01-2005 1 02-05-2007 1 31-08-2014 1 04-02-2004 1 11-06-2008 1 23-11-1997 1 19-08-1995 1 06-12-1993 1 08-02-1996 1 05-10-1991 1 26-09-1991 1 13-05-2001 1 10-05-2009 1 27-10-2001 1 08-05-1995 1 06-03-2003 1 01-07-2013 1 03-08-2009 1 27-07-1997 1 29-05-1992 1 07-12-1998 1 01-03-2010 1 26-02-2005 1 21-04-2006 1 26-08-2000 1 07-04-1998 1 31-10-1993 1 19-04-2007 1 12-10-1995 1 19-09-2004 1 20-12-1990 1 11-02-1991 1 13-11-1998 1 07-11-1994 1 19-06-2008 1 25-10-2012 1 04-03-2001 1 29-07-2012 1 17-01-2015 1 19-09-2012 1 14-07-2000 1 21-06-1994 1 13-03-2008 1 11-12-2009 1 23-10-2007 1 07-04-2014 1 22-02-1992 1 26-10-1996 1 12-12-1998 1 04-07-2007 1 19-05-1992 1 16-01-1996 1 27-01-2007 1 19-10-1992 1 24-08-2000 1 18-10-2000 1 01-06-2006 1 19-12-2004 1 27-11-2005 1 27-09-2011 1 11-07-2002 1 21-11-2006 1 02-04-1991 1 28-08-1995 1 04-07-2013 1 18-02-1990 1 08-04-2003 1 02-01-2001 1 12-12-1999 1 17-06-2006 1 05-01-2013 1 18-01-2014 1 17-12-2003 1 10-04-2013 1 05-03-2009 1 12-12-1991 1 31-03-2005 1 25-08-2012 1 22-10-2011 1 09-01-2002 1 14-11-2010 1 30-01-2005 1 28-02-2004 1 30-08-2000 1 05-08-1996 1 31-07-2011 1 09-12-2006 1 28-08-2005 1 22-02-1994 1 28-01-1998 1 01-11-1991 1 20-11-2005 1 08-03-1998 1 20-03-1992 1 15-08-2011 1 27-01-1991 1 20-08-1996 1 07-12-1992 1 14-01-1999 1 20-11-1997 1 28-10-2006 1 17-11-2004 1 07-10-1990 1 28-12-1998 1 05-07-2001 1 05-02-1994 1 04-03-2000 1 30-07-1992 1 13-02-1991 1 06-03-2010 1 03-06-2009 1 30-11-1993 1 08-07-1990 1 25-04-2002 1 01-09-1992 1 15-04-2004 1 11-03-2014 1 07-04-2010 1 16-05-2001 1 20-09-1999 1 24-01-2010 1 22-07-2004 1 25-11-2006 1 12-02-1998 1 21-08-1994 1 02-09-2011 1 01-05-2010 1 19-09-2009 1 19-10-2001 1 31-12-2003 1 11-02-1994 1 31-10-2013 1 02-02-1996 1 06-02-2009 1 16-11-2007 1 07-01-2011 1 27-08-2014 1 19-10-1999 1 07-07-1997 1 08-09-2009 1 10-09-2002 1 23-06-2012 1 20-07-2007 1 25-11-2002 1 08-03-2009 1 05-07-1999 1 17-05-1994 1 27-12-2000 1 15-02-1993 1 16-09-2009 1 26-10-2012 1 16-03-1998 1 24-06-2014 1 02-10-1997 1 24-09-2001 1 23-07-1992 1 30-06-2002 1 31-07-1999 1 07-12-2001 1 04-01-2002 1 28-11-1991 1 11-07-1996 1 17-06-2008 1 21-12-2013 1 10-09-2000 1 16-12-2011 1 03-10-2014 1 10-04-2009 1 28-07-2002 1 21-03-1997 1 06-09-2008 1 29-06-2002 1 07-08-1991 1 19-01-1996 1 24-04-2007 1 28-09-2011 1 12-10-1997 1 25-01-2008 1 08-06-2005 1 20-04-2004 1 27-02-2010 1 29-11-1999 1 23-08-2005 1 14-05-2001 1 03-04-2001 1 22-01-1993 1 05-01-2014 1 06-12-1991 1 29-04-2010 1 06-11-2013 1 29-02-1992 1 06-12-1995 1 09-08-1990 1 28-10-1998 1 07-11-1992 1 02-12-2012 1 13-04-2003 1 07-11-2012 1 13-12-2014 1 21-10-2005 1 19-06-1992 1 27-06-2008 1 11-10-2012 1 11-12-1998 1 02-11-2010 1 20-08-1994 1 03-11-1990 1 06-06-2014 1 01-05-1997 1 02-01-1991 1 01-01-2008 1 19-10-1990 1 11-08-1999 1 22-07-1999 1 30-08-2012 1 02-06-2010 1 21-02-1999 1 18-08-2006 1 18-01-1997 1 04-12-2013 1 24-05-2003 1 06-01-2011 1 05-01-1991 1 10-03-1991 1 16-08-2003 1 29-10-2006 1 04-08-1997 1 10-02-1997 1 26-08-2013 1 29-06-2009 1 15-08-1999 1 10-05-2012 1 27-06-1996 1 01-04-2002 1 14-11-2005 1 03-08-1993 1 23-03-2009 1 25-07-1995 1 16-03-2014 1 31-01-2011 1 16-06-1991 1 14-04-1993 1 15-07-2011 1 29-07-1995 1 16-09-2013 1 21-01-2009 1 25-04-2007 1 11-10-1994 1 20-04-2013 1 12-05-2007 1 02-02-2001 1 13-10-1991 1 03-09-1991 1 25-12-1997 1 14-02-1992 1 10-03-2004 1 29-09-2001 1 13-11-1995 1 22-04-2010 1 21-08-2010 1 24-10-1997 1 09-11-1991 1 22-02-2015 1 16-04-1995 1 18-06-2000 1 01-10-2001 1 24-07-2012 1 02-08-1992 1 17-03-2010 1 06-12-1996 1 10-08-2006 1 03-02-1994 1 03-02-1993 1 15-08-2002 1 05-12-2007 1 09-02-2007 1 08-02-1990 1 01-10-2006 1 16-03-2003 1 30-03-1999 1 14-02-1990 1 01-08-2010 1 20-01-1996 1 29-12-2010 1 30-04-2013 1 23-07-2014 1 09-11-1992 1 25-03-2007 1 16-06-2006 1 11-03-1996 1 29-10-1993 1 02-11-2012 1 03-06-1991 1 08-06-2002 1 18-06-2010 1 24-07-2007 1 18-06-1997 1 30-09-1993 1 28-03-1995 1 02-08-2006 1 28-08-1998 1 20-11-1991 1 25-12-1991 1 23-08-2009 1 24-07-1999 1 18-08-2007 1 18-02-2011 1 02-04-2002 1 05-05-1991 1 02-08-1991 1 10-04-1994 1 26-09-2010 1 29-02-2004 1 31-12-2012 1 25-02-2005 1 07-03-2005 1 01-11-2014 1 06-08-1994 1 12-08-2004 1 17-09-2003 1 27-12-2001 1 16-01-2013 1 24-12-2012 1 27-04-2012 1 29-11-2001 1 04-03-2014 1 21-08-2006 1 18-03-1990 1 03-04-2013 1 06-12-2006 1 23-07-1996 1 28-09-2007 1 20-11-2010 1 17-09-2011 1 20-11-2008 1 01-03-1991 1 13-08-2004 1 24-06-1998 1 30-08-1997 1 04-02-2011 1 17-04-2005 1 24-03-2006 1 28-03-2006 1 25-11-1991 1 13-01-1991 1 27-01-2000 1 08-10-1995 1 09-05-2008 1 20-02-2001 1 24-02-2012 1 13-12-1993 1 23-01-2003 1 22-07-2002 1 03-02-2008 1 04-06-1996 1 21-09-1990 1 13-12-1995 1 28-11-2002 1 18-05-1993 1 14-03-2012 1 28-01-1993 1 10-02-2002 1 05-11-2000 1 27-11-1992 1 18-10-2006 1 01-12-2000 1 03-02-2006 1 24-10-2007 1 19-09-2007 1 28-02-2010 1 11-10-1992 1 16-11-1991 1 04-03-2006 1 16-07-2014 1 28-02-1997 1 28-10-2007 1 17-07-2012 1 16-07-1991 1 03-08-1994 1 29-07-1996 1 28-09-1994 1 04-11-2004 1 28-07-2014 1 05-08-2005 1 29-07-2009 1 21-04-2001 1 10-02-2008 1 22-09-2002 1 04-11-2010 1 14-11-2007 1 01-06-1997 1 23-02-2014 1 24-05-2004 1 25-11-2004 1 10-02-1994 1 04-03-2003 1 15-02-2011 1 01-05-2013 1 03-01-2015 1 23-01-2013 1 11-09-2004 1 25-08-1990 1 26-06-2009 1 13-04-1991 1 28-12-1999 1 11-07-2001 1 09-11-2008 1 01-04-1994 1 21-04-2008 1 22-12-2012 1 20-05-1990 1 11-09-2010 1 17-04-1999 1 07-07-2013 1 06-02-2006 1 24-01-2009 1 30-06-2000 1 21-06-1997 1 29-03-2005 1 04-02-2013 1 16-05-1990 1 27-01-1990 1 28-12-2004 1 29-09-1997 1 07-05-1990 1 18-09-1999 1 08-05-2005 1 11-05-2010 1 02-08-2009 1 15-10-2005 1 26-01-2011 1 10-04-1996 1 23-04-1995 1 18-10-1995 1 16-12-1998 1 18-07-1991 1 14-09-2001 1 09-03-1999 1 24-06-2003 1 26-08-2012 1 01-03-1997 1 19-05-1993 1 06-07-1996 1 09-12-2005 1 22-09-1990 1 29-08-1999 1 06-11-2001 1 07-08-1994 1 11-02-2010 1 12-07-2006 1 06-10-2002 1 19-12-2011 1 10-06-2001 1 15-02-2001 1 23-08-1996 1 07-08-1992 1 05-07-2003 1 10-02-2007 1 28-12-2014 1 17-10-2014 1 21-07-1996 1 02-07-1991 1 07-01-2008 1 11-10-2003 1 13-02-2003 1 26-06-2000 1 16-07-2004 1 04-03-2007 1 09-10-2009 1 09-07-2013 1 14-03-2007 1 07-12-1993 1 12-02-2009 1 23-02-1993 1 28-01-1992 1 16-07-2003 1 24-06-2009 1 04-07-1997 1 18-04-1993 1 21-03-2008 1 03-02-1999 1 06-09-2000 1 11-11-2013 1 09-10-1995 1 25-10-2007 1 09-10-1990 1 20-03-1991 1 19-12-2001 1 08-11-2009 2 27-07-2014 2 25-12-2013 2 11-03-2010 2 15-05-1997 2 16-05-2008 2 28-12-2002 2 16-07-2002 2 20-07-1991 2 07-12-1995 2 04-06-2000 2 03-02-1997 2 28-01-2010 2 21-09-1996 2 09-08-2004 2 14-12-1991 2 04-05-2000 2 05-01-1992 2 19-09-1995 2 03-01-2004 2 07-04-1999 2 30-08-1993 2 28-12-1991 2 09-07-2002 2 29-09-1999 2 07-12-1999 2 21-09-2005 2 25-09-2001 2 05-07-2014 2 11-11-1998 2 15-11-1997 2 09-03-2003 2 20-09-1990 2 25-05-1990 2 07-07-1996 2 24-06-1990 2 21-12-2002 2 06-05-2007 2 14-07-1997 2 07-11-1997 2 22-08-1991 2 14-04-1992 2 29-01-1998 2 05-08-1992 3 28-04-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INSURED_RELATIONSHIP : 6 unmarried 141 wife 155 husband 170 not-in-family 174 other-relative 177 own-child 183 Name: insured_relationship, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 07-02-2015 10 25-01-2015 10 11-02-2015 10 10-02-2015 10 19-02-2015 10 29-01-2015 11 26-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 27-01-2015 13 23-01-2015 13 03-02-2015 13 09-02-2015 13 20-02-2015 14 27-02-2015 14 22-01-2015 14 15-01-2015 15 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-02-2015 16 16-01-2015 16 16-02-2015 16 05-02-2015 16 13-02-2015 16 06-01-2015 17 09-01-2015 17 24-02-2015 17 08-02-2015 17 26-02-2015 17 20-01-2015 18 28-02-2015 18 14-02-2015 18 18-01-2015 18 03-01-2015 18 25-02-2015 18 01-02-2015 18 14-01-2015 19 01-01-2015 19 21-01-2015 19 12-01-2015 19 23-02-2015 19 21-02-2015 19 31-01-2015 20 12-02-2015 20 22-02-2015 20 06-02-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 10-01-2015 24 04-02-2015 24 24-01-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_TYPE : 4 Parked Car 84 Vehicle Theft 94 Single Vehicle Collision 403 Multi-vehicle Collision 419 Name: incident_type, dtype: int64 COLLISION_TYPE : 4 Unknown 178 Front Collision 254 Side Collision 276 Rear Collision 292 Name: collision_type, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 3340 3rd Hwy 1 3457 Texas Lane 1 9103 MLK Lane 1 1507 Solo Ave 1 4653 Pine St 1 6684 Solo Lane 1 9580 MLK Ave 1 3006 Lincoln Ridge 1 1358 Maple St 1 3029 5th Ave 1 2217 Tree Lane 1 4335 1st St 1 5189 Francis Drive 1 1553 Lincoln St 1 3660 Andromedia Hwy 1 5812 Weaver Ave 1 5499 Elm Hwy 1 4567 Pine Ave 1 7511 1st Ave 1 4710 Lincoln Hwy 1 5778 Pine Ridge 1 4143 Maple Ridge 1 4175 Elm Ridge 1 8281 Lincoln Lane 1 1725 Solo Lane 1 3929 Elm Ave 1 9787 Andromedia Ave 1 5894 Flute Drive 1 2100 Francis Drive 1 1298 Maple Hwy 1 2774 Apache Drive 1 8617 Best Ave 1 9720 Lincoln Hwy 1 3495 Britain Drive 1 6359 MLK Ridge 1 1331 Elm Ridge 1 6655 5th Drive 1 9988 Rock Ridge 1 6582 Elm Lane 1 2290 4th Ave 1 1529 Elm Ridge 1 5678 Lincoln Drive 1 9397 5th Hwy 1 1562 Britain St 1 9439 MLK St 1 7240 5th Ridge 1 5790 Flute Ridge 1 2873 Flute Ave 1 3592 MLK Ridge 1 3221 Solo Ridge 1 4239 Weaver Ave 1 7523 Oak Lane 1 2889 Francis St 1 9523 Solo Hwy 1 6375 2nd Lane 1 7314 Tree Drive 1 9278 Francis Ridge 1 5201 Texas Hwy 1 7601 Andromedia Lane 1 2644 Elm Drive 1 4972 Francis Lane 1 2123 MLK Ridge 1 2204 Washington Lane 1 9239 Washington Ridge 1 7846 Andromedia Drive 1 9760 Solo Lane 1 2577 Texas Ridge 1 8876 1st St 1 5028 Maple Ridge 1 9105 Tree Lane 1 2654 Embaracadero St 1 6834 1st Drive 1 3553 Texas Ave 1 6399 Oak Drive 1 6179 3rd Ridge 1 5821 2nd St 1 7628 4th Lane 1 7488 Lincoln Lane 1 4857 Weaver St 1 2920 5th Ave 1 8118 Elm Ridge 1 8576 Andromedia St 1 5104 Francis Drive 1 3929 Oak Drive 1 1989 Solo Lane 1 8864 Tree Ridge 1 6250 1st Ridge 1 2654 Elm Drive 1 1655 Francis Hwy 1 3926 Rock Lane 1 4642 Rock Ridge 1 9214 Texas Drive 1 6149 Best Ridge 1 4814 Lincoln Lane 1 5071 Flute Ridge 1 1833 Solo Ave 1 4232 Britain Ridge 1 5783 Oak Ave 1 4939 Best St 1 6956 Maple Drive 1 2968 Andromedia Ave 1 1364 Best St 1 6618 Cherokee Drive 1 6092 5th Ave 1 5474 Weaver Hwy 1 1580 Maple Lane 1 8269 Sky Hwy 1 2324 Texas Ridge 1 4546 Tree St 1 4964 Elm Lane 1 5994 5th Ave 1 8014 Embaracadero Drive 1 4154 Lincoln Hwy 1 3966 Francis Ridge 1 5838 Pine Lane 1 6678 Weaver Drive 1 3884 Pine Lane 1 8211 Sky Hwy 1 9418 5th Hwy 1 5071 1st Lane 1 6378 Britain Ave 1 5971 5th Hwy 1 1173 Andromedia Ave 1 6158 Sky Ridge 1 4633 5th Lane 1 1437 3rd Lane 1 7900 Sky Hwy 1 4699 Texas Ridge 1 8080 Oak Lane 1 9214 Elm Ridge 1 8214 Flute St 1 1679 2nd Hwy 1 2272 Embaracadero Drive 1 1328 Texas Lane 1 2275 Best Lane 1 8100 3rd Ave 1 3567 4th Drive 1 3998 Flute St 1 3540 Maple St 1 6309 Cherokee Ave 1 3311 2nd Drive 1 8336 1st Ridge 1 3796 Cherokee Drive 1 6550 Andromedia St 1 6751 5th Hwy 1 8983 Tree St 1 3416 Washington Drive 1 9038 2nd Lane 1 3087 Oak Hwy 1 7756 Solo Drive 1 1824 5th Lane 1 1316 Britain Ridge 1 7583 Washington Ave 1 7098 Lincoln Hwy 1 1267 Francis Hwy 1 7825 1st Ridge 1 7721 Washington Ridge 1 5279 Pine Ridge 1 8492 Andromedia Ridge 1 6957 Weaver Drive 1 3618 Sky Ave 1 6408 Weaver Ridge 1 7630 Rock Drive 1 7253 MLK St 1 4866 4th Hwy 1 3770 Flute Drive 1 6981 Weaver St 1 4492 Andromedia Ave 1 6440 Rock Lane 1 1353 Washington St 1 4486 Cherokee Ridge 1 7575 Pine St 1 3220 Rock Drive 1 2725 Britain Ridge 1 6409 Cherokee Drive 1 7649 Texas St 1 9818 Cherokee Ave 1 8538 Texas Lane 1 6206 3rd Ridge 1 3184 Oak Ave 1 9685 Sky Ridge 1 5771 Best St 1 7434 Oak Hwy 1 7644 Tree Ridge 1 2526 Embaracadero Ave 1 7061 Cherokee Drive 1 1102 Apache Hwy 1 2862 Tree Ridge 1 6723 Best Drive 1 1248 MLK Ridge 1 3982 Washington Hwy 1 5806 Embaracadero St 1 9067 Texas Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 6619 Flute Ave 1 7295 Tree Hwy 1 3320 5th Hwy 1 2603 Andromedia Hwy 1 6874 Maple Ridge 1 4862 Lincoln Hwy 1 3419 Apache St 1 5608 Solo St 1 9240 Britain Ave 1 1416 Cherokee Ridge 1 4672 MLK St 1 9821 Francis Ave 1 2193 4th Ridge 1 8689 Maple Hwy 1 7859 4th Ridge 1 7928 Maple Ridge 1 5211 Weaver Drive 1 4907 Andromedia Drive 1 5639 1st Ridge 1 9706 MLK Lane 1 4538 Flute Hwy 1 9633 MLK Lane 1 7709 Rock Lane 1 6939 3rd Hwy 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 2457 Washington Ave 1 7773 Tree Hwy 1 9373 Pine Hwy 1 3492 Flute Lane 1 7082 Oak Ridge 1 8822 Sky St 1 9911 Britain Lane 1 9728 Britain Hwy 1 6058 Andromedia Hwy 1 4460 4th Lane 1 5719 2nd Lane 1 9177 Texas Ave 1 9730 2nd Hwy 1 6985 Maple Lane 1 6467 Best Ave 1 6838 Flute Lane 1 1810 Elm Hwy 1 2500 Tree St 1 6971 Best Ridge 1 6859 Flute Ridge 1 8404 Embaracadero St 1 9633 4th St 1 2469 Francis Lane 1 2492 Lincoln Lane 1 1830 Sky St 1 2220 1st Lane 1 6317 Best St 1 6848 Elm Hwy 1 8917 Tree Ridge 1 2950 MLK Ave 1 7576 Pine Ridge 1 9744 Texas Drive 1 1110 4th Drive 1 5312 Francis Ridge 1 6530 Weaver Ave 1 4955 Lincoln Ridge 1 1365 Francis Ave 1 4237 4th St 1 6604 Apache Drive 1 7797 Tree Ridge 1 8758 5th St 1 8907 Tree Ave 1 2798 1st Ave 1 9417 Tree Hwy 1 2333 Maple Lane 1 4618 Flute Ave 1 8906 Elm Lane 1 4755 Best Lane 1 5846 Weaver Drive 1 2005 Texas Hwy 1 6259 Weaver St 1 1491 Francis Ridge 1 2381 1st Hwy 1 4614 MLK Ave 1 9360 3rd Drive 1 2037 5th Drive 1 2352 Sky Drive 1 7877 3rd Ridge 1 8742 4th St 1 6738 Washington Hwy 1 5780 4th Ave 1 3039 Oak Hwy 1 7897 Lincoln St 1 8456 1st Ave 1 6706 Francis Drive 1 1320 Flute Lane 1 1840 Embaracadero Ave 1 9138 1st St 1 8212 Flute Ridge 1 8548 Cherokee Ridge 1 7791 Britain Ridge 1 9935 4th Drive 1 6355 4th Hwy 1 9588 Solo St 1 6068 2nd St 1 3167 4th Ridge 1 5333 MLK Lane 1 3028 5th St 1 8493 Apache Drive 1 9070 Tree Ave 1 1956 Apache St 1 8198 Embaracadero Lane 1 1331 Britain Hwy 1 8782 3rd St 1 9737 Solo Hwy 1 5418 Britain Ave 1 5663 Oak Lane 1 3841 Washington Lane 1 6934 Lincoln Ave 1 2533 Elm St 1 3915 Embaracadero St 1 3834 Pine St 1 5475 Rock Lane 1 6889 Cherokee St 1 7466 MLK Ridge 1 4872 Rock Ridge 1 8834 Elm Drive 1 5341 5th Ave 1 5904 1st Drive 1 2376 Sky Ridge 1 7108 Tree St 1 7380 5th Hwy 1 3936 Tree Drive 1 6451 1st Hwy 1 5650 Sky Drive 1 6668 Andromedia Ridge 1 4625 MLK Drive 1 1469 Lincoln Drive 1 3998 4th Hwy 1 1951 Best Ave 1 8368 Cherokee Ave 1 8624 Francis Ave 1 4394 Oak St 1 8991 Texas Hwy 1 7930 Texas Ave 1 2617 Andromedia Drive 1 8006 Maple Hwy 1 9639 Britain Ridge 1 2820 Britain St 1 4519 Embaracadero St 1 7327 Lincoln Drive 1 2123 Texas Ave 1 8453 Elm St 1 3753 Francis Lane 1 6137 MLK St 1 8949 Rock Hwy 1 4475 Lincoln Ridge 1 1546 Cherokee Ave 1 7571 Elm Ridge 1 5168 5th Ave 1 7973 4th St 1 4119 Texas St 1 3797 Solo Lane 1 4885 Oak Lane 1 1919 4th Lane 1 9082 3rd Lane 1 4434 Weaver St 1 1515 Embaracadero St 1 7042 Maple Ridge 1 3952 Andromedia Lane 1 3925 Sky St 1 2299 1st St 1 6751 Pine Ridge 1 8203 Lincoln Ave 1 3697 Apache Drive 1 3818 Texas Ridge 1 1123 5th Lane 1 4242 Rock Lane 1 4702 Texas Drive 1 3656 Solo Ave 1 9286 Oak Ave 1 8701 5th Lane 1 4981 Weaver St 1 8306 1st Drive 1 7745 Washington Ridge 1 1386 Britain St 1 5969 Francis St 1 7168 Andromedia Ridge 1 1472 4th Drive 1 6676 Tree Lane 1 8941 Solo Ridge 1 1186 Rock St 1 2537 5th Ave 1 2804 Best St 1 6581 Rock Ridge 1 1275 4th Ridge 1 9942 Tree Ave 1 3196 Cherokee St 1 2318 Washington Hwy 1 3376 5th Drive 1 6851 3rd Drive 1 2865 Maple Lane 1 4347 2nd Ridge 1 9682 Cherokee Ridge 1 1213 4th Lane 1 9352 Washington Ave 1 6256 Elm St 1 5226 Maple St 1 1910 Sky Ave 1 1815 Cherokee Drive 1 4574 Britain Hwy 1 2280 4th Ave 1 6888 Elm Ridge 1 7397 4th Drive 1 2757 4th Hwy 1 8078 Britain Hwy 1 3127 Flute St 1 1346 5th Lane 1 1532 Washington St 1 6501 5th Drive 1 8940 Elm Ave 1 5649 Texas Ave 1 9719 4th Lane 1 1273 Rock Lane 1 3842 Solo Ridge 1 8766 Lincoln Lane 1 7236 Apache Lane 1 7544 Washington Ave 1 8096 Apache Hwy 1 3525 3rd Hwy 1 4826 5th St 1 9224 Sky Drive 1 4020 Best Drive 1 3809 Texas Lane 1 7495 Washington Ave 1 9020 Elm Ave 1 2808 Elm St 1 6492 4th Lane 1 2003 Maple Hwy 1 3618 Maple Lane 1 9657 5th Ave 1 8805 Cherokee Drive 1 3061 Francis Hwy 1 8621 Best Ridge 1 6605 Tree Ave 1 9573 Weaver Ave 1 8101 3rd Ridge 1 7460 Apache Lane 1 5318 5th Ave 1 8638 3rd Ave 1 7693 Cherokee Lane 1 3066 Francis Ave 1 4835 Britain Ridge 1 4629 Elm Ridge 1 1705 Weaver St 1 6309 5th Ave 1 2397 Cherokee Ave 1 6658 Weaver St 1 9742 5th Ridge 1 9034 Weaver Ridge 1 8381 Solo Hwy 1 9316 Pine Ave 1 5630 1st Drive 1 5997 Embaracadero Drive 1 8832 Pine Drive 1 6677 Andromedia Drive 1 4994 Lincoln Drive 1 9847 Elm St 1 5602 Britain St 1 1809 Sky St 1 1555 Washington Lane 1 1916 Elm St 1 7782 Rock St 1 5532 Francis Lane 1 6741 Oak Ridge 1 7693 Britain Lane 1 1128 Maple Lane 1 5280 Pine Ave 1 5532 Weaver Ridge 1 9816 Britain St 1 8204 Pine Lane 1 4627 Elm Ridge 1 3092 Texas Drive 1 1914 Francis St 1 5925 Tree Hwy 1 3397 5th Ave 1 7281 Oak St 1 4447 Francis Hwy 1 1845 Best St 1 1325 1st Lane 1 2697 Oak Drive 1 9724 Maple St 1 7121 Britain Drive 1 8542 Lincoln Ridge 1 5061 Francis Ave 1 2777 Solo Drive 1 6493 Lincoln Lane 1 1953 Sky Lane 1 1618 Maple Hwy 1 1371 Texas Lane 1 6193 1st Hwy 1 3653 Elm Drive 1 4429 Washington St 1 3707 Oak Ridge 1 3664 Francis Ridge 1 8667 Weaver Lane 1 7477 MLK Drive 1 2903 Weaver Drive 1 7582 Pine Drive 1 4369 Maple Lane 1 5191 4th St 1 4585 Francis Ave 1 4176 Britain Hwy 1 3847 Elm Hwy 1 2832 Andromedia Lane 1 6931 Elm St 1 4755 1st St 1 2199 Texas Drive 1 7976 Britain Drive 1 8489 Pine Hwy 1 8983 Francis Ridge 1 4876 Washington Drive 1 7705 Lincoln Drive 1 6516 Solo Drive 1 4214 MLK Ridge 1 4231 3rd Ave 1 1738 Solo Lane 1 5743 4th Ridge 1 8212 Rock Ave 1 2823 Weaver Lane 1 3052 Weaver Ridge 1 7783 Lincoln Hwy 1 6515 Oak Lane 1 5779 2nd Lane 1 8021 Flute Ave 1 3414 Elm Ave 1 3995 Lincoln Hwy 1 7733 Britain Lane 1 9322 Rock Hwy 1 9325 Lincoln Drive 1 1558 1st Ridge 1 3275 Pine St 1 6428 Andromedia Lane 1 2711 Britain Ave 1 9573 2nd Ave 1 3439 Andromedia Hwy 1 7909 Andromedia Hwy 1 7684 Francis Ridge 1 8509 Apache St 1 4554 Sky Ave 1 6435 Texas Ave 1 8845 5th Ave 1 3288 Tree Lane 1 8926 Texas Ridge 1 3799 Embaracadero Drive 1 2756 Britain Hwy 1 5874 1st Hwy 1 7331 Sky Hwy 1 8821 Elm St 1 9422 Washington Ridge 1 2986 MLK Drive 1 9633 Rock Hwy 1 2651 MLK Lane 1 3263 Pine Ridge 1 6583 MLK Ridge 1 6608 Apache Lane 1 1699 Oak Drive 1 6315 2nd Lane 1 1012 5th Lane 1 8954 Apache Lane 1 3102 Apache St 1 3903 Oak Ave 1 4188 Britain Ave 1 3246 Britain Ridge 1 7780 Flute Lane 1 4577 Sky Hwy 1 2430 MLK Ave 1 7281 Maple Hwy 1 8655 Cherokee Lane 1 5584 Britain Lane 1 8964 Francis St 1 8188 Tree Ave 1 6303 1st Drive 1 3693 Pine Ave 1 9878 Washington Ave 1 6011 Britain St 1 6456 Andromedia Drive 1 2311 4th St 1 8215 Flute Drive 1 8639 5th Hwy 1 3751 Tree Hwy 1 6724 Andromedia St 1 7756 Pine Hwy 1 3486 Flute Ave 1 4780 Best Drive 1 5431 3rd Ridge 1 7740 MLK St 1 5459 MLK Ave 1 1240 Tree Lane 1 9489 3rd St 1 7002 Oak Hwy 1 1628 Best Drive 1 6045 Andromedia St 1 1220 MLK Ave 1 7316 Texas Ave 1 5985 Lincoln Lane 1 6128 Elm Lane 1 9153 3rd Hwy 1 8862 Maple Ridge 1 8081 Flute Ridge 1 4098 Weaver Ridge 1 3089 Oak Ridge 1 5771 Sky Ave 1 6603 Francis Hwy 1 8920 Best Ave 1 8492 Weaver Hwy 1 3327 Lincoln Drive 1 4264 Lincoln Ridge 1 1992 Britain Drive 1 1762 Maple Hwy 1 4279 Solo Drive 1 4431 Rock St 1 5839 Weaver Lane 1 2900 Sky Drive 1 2914 Oak Drive 1 1589 Pine St 1 1880 Weaver Drive 1 3900 Texas St 1 4793 4th Ridge 1 9154 MLK Hwy 1 3104 Sky Drive 1 4558 3rd Hwy 1 5051 Elm St 1 1536 Flute Drive 1 5058 4th Lane 1 6681 Texas Ridge 1 4693 Lincoln Hwy 1 6494 4th Ave 1 1269 Flute Drive 1 7574 4th St 1 8879 1st Drive 1 3097 4th Drive 1 6110 Rock Ridge 1 4985 Sky Lane 1 4477 5th Ave 1 2293 Washington Ave 1 4782 Sky Lane 1 7717 Britain Hwy 1 4937 Flute Drive 1 1941 5th Ridge 1 8085 Andromedia St 1 5914 Oak Ave 1 5074 3rd St 1 5383 Maple Drive 1 4053 Sky Lane 1 6853 Sky Hwy 1 5007 Oak St 1 9875 MLK Ave 1 4910 1st Lane 1 7609 Rock St 1 1215 Pine Hwy 1 7299 Apache St 1 6574 4th Drive 1 7877 Sky Lane 1 2078 3rd Ave 1 4390 4th Drive 1 9751 Sky Ridge 1 5765 Washington St 1 5480 3rd Ridge 1 6738 Francis Hwy 1 1388 Embaracadero Hwy 1 3492 Britain St 1 6955 Pine Drive 1 5178 Weaver Hwy 1 5812 3rd Hwy 1 9138 3rd St 1 9317 Apache Ave 1 3878 Tree Lane 1 9748 Sky Drive 1 2644 MLK Drive 1 4905 Best Lane 1 9734 2nd Ridge 1 5622 Best Ridge 1 9751 Tree St 1 7459 Flute St 1 4158 Washington Lane 1 5249 4th Ave 1 3808 5th Ave 1 6044 Weaver Drive 1 7223 Embaracadero St 1 3846 4th Hwy 1 3790 Andromedia Hwy 1 3508 Washington St 1 6719 Flute St 1 9562 4th Ridge 1 3706 4th Hwy 1 3098 Oak Lane 1 9910 Maple Ave 1 1512 Rock Lane 1 3422 Flute St 1 1643 Washington Hwy 1 5621 4th Ave 1 2509 Rock Drive 1 5586 2nd St 1 6634 Texas Ridge 1 9369 Flute Hwy 1 3423 Francis Ave 1 3172 Tree Ridge 1 1030 Pine Lane 1 7142 5th Lane 1 1832 Elm Hwy 1 5093 Flute Lane 1 3171 Andromedia Lane 1 7178 Best Drive 1 8150 Washington Ridge 1 5540 Sky St 1 5585 Washington Drive 1 7121 Rock St 1 1879 4th Lane 1 7066 Texas Ave 1 2886 Tree Ridge 1 9798 Sky Ridge 1 1589 Best Ave 1 3659 Oak Lane 1 4981 Flute Hwy 1 5260 Francis Drive 1 9358 Texas Ridge 1 3289 Britain Drive 1 7819 2nd Ave 1 6067 Weaver Ridge 1 4687 5th Drive 1 3323 1st Lane 1 5160 2nd Hwy 1 4931 Maple Drive 1 7393 Washington St 1 7502 Rock Lane 1 9397 Francis St 1 1923 2nd Hwy 1 3814 Britain Drive 1 3100 Best St 1 1929 Britain Drive 1 4095 MLK St 1 3966 Oak Hwy 1 8973 Washington St 1 5455 Oak Hwy 1 8995 1st Ave 1 5812 Oak St 1 7069 4th Hwy 1 2577 Washington Drive 1 2494 Andromedia Drive 1 8049 4th St 1 1681 Cherokee Hwy 1 4254 Best Ridge 1 5782 Rock Drive 1 7447 Lincoln Ridge 1 4983 MLK Ridge 1 7041 Tree Ridge 1 5640 Embaracadero Lane 1 5380 Pine St 1 7816 MLK Lane 1 7701 Tree St 1 1707 Sky Ave 1 2087 Apache Ave 1 7144 Andromedia St 1 6390 Apache St 1 4539 Texas St 1 5865 Sky Lane 1 4055 2nd Drive 1 2849 Pine Drive 1 2889 Weaver St 1 5277 Texas Lane 1 8668 Flute St 1 8618 Texas Lane 1 3930 Embaracadero St 1 2905 Embaracadero Drive 1 9236 2nd Hwy 1 6443 Washington Ridge 1 9603 Texas Lane 1 6717 Best Drive 1 8167 Apache Ave 1 1376 Pine St 1 6731 Andromedia Hwy 1 4721 Cherokee Hwy 1 2253 Maple Ave 1 9918 Andromedia Drive 1 5276 2nd Lane 1 9007 Francis Hwy 1 5352 Lincoln Drive 1 8245 4th Hwy 1 6660 MLK Drive 1 4545 4th Ridge 1 9101 2nd Hwy 1 8770 1st Lane 1 1957 Washington Ave 1 6608 MLK Hwy 1 7121 Francis Lane 1 6357 Texas Lane 1 4615 Embaracadero Ave 1 7552 3rd St 1 4296 Pine Hwy 1 1218 Sky Hwy 1 3769 Sky St 1 7954 Tree Ridge 1 5862 Apache Ridge 1 3771 4th St 1 1578 5th Lane 1 1985 5th Ave 1 8097 Maple Lane 1 7428 Sky Hwy 1 8602 Washington Ridge 1 8353 Britain Ridge 1 3122 Apache Drive 1 6638 Tree Drive 1 8125 Texas Ridge 1 4414 Solo Drive 1 3777 Maple Ave 1 4268 2nd Ave 1 1229 5th Ave 1 7238 2nd St 1 3227 Maple Ave 1 1306 Andromedia St 1 5901 Elm Drive 1 6702 Andromedia St 1 1381 Francis Ave 1 2299 Britain Drive 1 6945 Texas Hwy 1 8718 Apache Lane 1 3658 Rock Drive 1 6035 Rock Ave 1 7476 4th St 1 3041 3rd Ave 1 9245 Weaver Ridge 1 4107 MLK Ridge 1 6522 Apache Drive 1 6117 4th Ave 1 7923 Elm Ave 1 1741 Best Ridge 1 1806 Weaver Ridge 1 3488 Flute Lane 1 2539 Embaracadero Ridge 1 1133 Apache St 1 1654 Pine St 1 5769 Texas Lane 1 8704 Britain Lane 1 5868 Best Drive 1 6104 Oak Ave 1 2668 Cherokee St 1 9488 Best Drive 1 9856 Apache St 1 7629 5th St 1 6331 MLK Ave 1 8579 Apache Drive 1 2289 Weaver Ridge 1 6429 4th Hwy 1 9081 Cherokee Hwy 1 7535 5th Lane 1 8477 Francis Hwy 1 7201 Washington Ave 1 2230 1st St 1 9610 Cherokee St 1 3743 Andromedia Ridge 1 6479 Francis Ave 1 3418 Texas Lane 1 2086 Francis Drive 1 7197 2nd Drive 1 8459 Apache Ave 1 9043 Maple Hwy 1 9760 4th Hwy 1 4291 Sky Hwy 1 9078 Francis Ridge 1 2003 2nd Hwy 1 8586 1st Ridge 1 1091 1st Drive 1 3053 Lincoln Drive 1 9148 4th Hwy 1 2100 MLK St 1 8809 Flute St 1 1620 Oak Ave 1 7529 Solo Ridge 1 5269 Flute Hwy 1 2337 Lincoln Hwy 1 4453 Best Ave 1 5650 Rock Ave 1 5236 Weaver Drive 1 2878 Britain Hwy 1 2014 Rock Ave 1 9066 Best Ridge 1 1818 Tree St 1 5868 Sky Hwy 1 8064 4th Ave 1 3555 Francis Ridge 1 9611 Pine Ridge 1 4671 5th Ridge 1 6329 Apache Ave 1 9929 Rock Drive 1 4234 Cherokee Lane 1 2646 MLK Drive 1 9484 Pine Drive 1 7500 Texas Ridge 1 3177 MLK Ridge 1 4116 Embaracadero Lane 1 7162 Maple Ave 1 8749 Tree St 1 7570 Cherokee Drive 1 4496 Pine Lane 1 3639 Flute Hwy 1 1028 Sky Lane 1 1454 5th Ridge 1 8957 Weaver Drive 1 1087 Flute Drive 1 6484 Tree Drive 1 3447 Solo Ave 1 2306 5th Lane 1 8811 Maple Hwy 1 7615 Weaver Drive 1 7405 Oak St 1 8524 Pine Lane 1 9529 4th Drive 1 8897 Sky St 1 7504 Flute Drive 1 9293 Pine Lane 1 3550 Washington Ave 1 2753 Cherokee Ave 1 7155 Apache Drive 1 4939 Oak Lane 1 2980 Sky Ridge 1 7551 Britain Lane 1 3805 Lincoln Hwy 1 2787 MLK St 1 5924 Maple Drive 1 6165 Rock Ridge 1 3167 2nd St 1 6536 MLK Hwy 1 1540 Apache Lane 1 7835 Cherokee Hwy 1 3094 Best Lane 1 3982 Weaver Lane 1 4995 Weaver Ridge 1 9980 Lincoln Ave 1 3590 Best Hwy 1 3443 Maple Ridge 1 2809 Francis Lane 1 2063 Weaver St 1 5455 Tree Ridge 1 7135 Flute Lane 1 6384 5th Ridge 1 5499 Flute Ridge 1 6260 5th Lane 1 9109 Britain Drive 1 8442 Britain Hwy 1 7705 Best Ridge 1 2850 Washington St 1 5445 Tree Hwy 1 1126 Texas Hwy 1 7534 MLK Hwy 1 8872 Oak Ridge 1 1515 Pine Lane 1 9169 Pine Ridge 1 6259 Lincoln Hwy 1 8946 2nd Drive 1 5363 Weaver Lane 1 5224 5th Lane 1 4272 Oak Ridge 1 1422 Flute Ave 1 7828 Cherokee Ave 1 3726 MLK Hwy 1 1617 Rock Drive 1 3110 Lincoln Lane 1 9279 Oak Hwy 1 3835 5th Ave 1 4434 Lincoln Ave 1 3654 Cherokee Ave 1 9169 Cherokee Hwy 1 6191 Oak Lane 1 3282 4th Lane 1 4058 Tree Drive 1 4652 Flute Drive 1 2201 4th Lane 1 8991 Embaracadero Ave 1 1687 3rd Lane 1 5022 1st St 1 5855 Apache St 1 3872 5th Drive 1 7819 Oak St 1 7426 Rock Drive 1 7112 Weaver Ave 1 2117 Lincoln Hwy 1 1135 Solo Lane 1 5053 Tree Drive 1 5506 Best St 1 2696 Cherokee Ridge 1 6770 1st St 1 9794 Embaracadero St 1 4905 Francis Ave 1 9879 Apache Drive 1 5483 Francis Drive 1 2048 3rd Ridge 1 2733 Texas Drive 1 8233 Tree Drive 1 2215 Best Ave 1 2942 1st Lane 1 4965 MLK Drive 1 5124 Maple St 1 1598 3rd Drive 1 5667 4th Drive 1 7785 Lincoln Lane 1 2352 MLK Drive 1 3706 Texas Hwy 1 8917 Cherokee Lane 1 Name: incident_location, dtype: int64 PROPERTY_DAMAGE : 3 YES 302 NO 338 Unknown 360 Name: property_damage, dtype: int64 AUTO_MODEL : 39 RSX 12 Accord 13 M5 15 X6 16 C300 18 3 Series 18 ML350 20 TL 20 CRV 20 Impreza 20 Corolla 20 Fusion 21 Civic 22 Silverado 22 Highlander 22 Ultima 23 X5 23 Maxima 24 Tahoe 24 Escape 24 93 25 Grand Cherokee 25 E400 27 95 27 F150 27 92x 28 Forrestor 28 Camry 28 Malibu 30 Pathfinder 31 Legacy 32 A5 32 Passat 33 Jetta 35 MDX 36 A3 37 Neon 37 Wrangler 42 RAM 43 Name: auto_model, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.property_damage.value_counts()
Unknown 360 NO 338 YES 302 Name: property_damage, dtype: int64
fraud_con['property_damage'] = fraud_con['property_damage'].astype('category')
fraud_con['property_damage'] = fraud_con['property_damage'].cat.codes
fraud_con.property_damage.value_counts()
1 360 0 338 2 302 Name: property_damage, dtype: int64
fraud_con.auto_model.value_counts()
RAM 43 Wrangler 42 Neon 37 A3 37 MDX 36 Jetta 35 Passat 33 A5 32 Legacy 32 Pathfinder 31 Malibu 30 Camry 28 Forrestor 28 92x 28 F150 27 95 27 E400 27 93 25 Grand Cherokee 25 Escape 24 Maxima 24 Tahoe 24 X5 23 Ultima 23 Highlander 22 Silverado 22 Civic 22 Fusion 21 TL 20 ML350 20 Corolla 20 CRV 20 Impreza 20 3 Series 18 C300 18 X6 16 M5 15 Accord 13 RSX 12 Name: auto_model, dtype: int64
fraud_con['auto_model'] = fraud_con['auto_model'].astype('category')
fraud_con['auto_model'] = fraud_con['auto_model'].cat.codes
fraud_con.auto_model.value_counts()
30 43 36 42 4 37 27 37 23 36 20 35 28 33 5 32 21 32 29 31 25 30 1 28 15 28 9 28 14 27 12 27 3 27 17 25 2 25 26 24 13 24 34 24 37 23 35 23 18 22 32 22 10 22 16 21 24 20 33 20 11 20 8 20 19 20 0 18 7 18 38 16 22 15 6 13 31 12 Name: auto_model, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 06-09-2005 1 30-04-2002 1 19-05-2014 1 21-05-2010 1 09-08-1993 1 12-10-2002 1 20-06-2002 1 28-04-2013 1 15-04-2013 1 01-03-2012 1 19-01-1998 1 28-07-2008 1 04-12-1995 1 20-02-2009 1 28-04-1994 1 24-05-2001 1 19-09-1991 1 20-07-1990 1 13-06-1994 1 10-01-2012 1 08-03-1995 1 10-02-1998 1 19-07-2006 1 11-01-2009 1 12-01-2012 1 03-09-2008 1 11-07-1991 1 29-08-2010 1 09-06-2001 1 05-07-2007 1 06-06-2002 1 05-07-2000 1 26-11-2001 1 05-02-2006 1 27-04-2009 1 08-07-2001 1 05-06-2011 1 23-04-2008 1 11-08-2007 1 15-11-2000 1 17-02-2010 1 01-03-2002 1 03-05-1991 1 17-11-2009 1 05-05-1998 1 10-12-2014 1 13-06-1992 1 29-05-1999 1 05-12-2014 1 25-04-2014 1 03-12-2005 1 05-12-1995 1 21-02-2006 1 27-10-2013 1 16-10-2002 1 12-10-1994 1 20-02-1998 1 05-02-1991 1 18-10-2005 1 18-11-2011 1 27-02-1994 1 22-03-2000 1 08-01-1994 1 08-06-1994 1 18-12-1994 1 19-03-1992 1 29-08-1995 1 06-06-2001 1 12-01-2010 1 29-03-2004 1 13-09-2003 1 08-04-1991 1 26-03-1995 1 15-07-1990 1 12-11-2009 1 01-02-1990 1 11-05-1996 1 23-06-2000 1 17-06-2009 1 24-04-1995 1 27-09-1990 1 03-03-1997 1 05-11-2008 1 02-09-2014 1 21-04-2003 1 03-11-1996 1 17-08-2014 1 15-09-1990 1 31-10-1997 1 03-10-1994 1 30-12-2002 1 06-03-2005 1 20-05-1995 1 16-07-1998 1 11-12-1992 1 09-04-1999 1 19-03-2014 1 15-12-1996 1 25-01-1999 1 08-02-1991 1 05-09-1996 1 10-11-2005 1 10-06-2014 1 18-11-1994 1 05-02-1997 1 30-08-2014 1 25-11-2009 1 12-09-1994 1 23-10-1996 1 13-11-2002 1 19-05-1990 1 19-04-2002 1 14-02-1994 1 16-09-1990 1 18-08-1990 1 15-02-1990 1 18-10-2012 1 18-06-2011 1 05-01-2012 1 23-08-2003 1 22-12-1995 1 09-12-1992 1 14-03-1995 1 21-04-1997 1 09-04-2003 1 15-03-2007 1 17-12-1995 1 03-12-2012 1 12-08-1999 1 15-08-1990 1 08-04-1996 1 18-12-1996 1 17-09-1994 1 04-06-2012 1 30-01-1992 1 18-02-1997 1 12-12-2011 1 23-08-1994 1 17-07-2005 1 10-03-1997 1 31-05-2004 1 25-02-2011 1 09-07-2001 1 01-08-2003 1 07-02-2007 1 05-04-2002 1 10-12-2005 1 18-06-1993 1 08-08-2010 1 18-11-1993 1 24-04-2013 1 28-04-2007 1 29-11-2009 1 11-06-2014 1 26-03-1990 1 20-06-2012 1 28-06-2010 1 16-07-1995 1 29-11-2004 1 29-09-2005 1 21-04-2010 1 28-03-1990 1 09-02-1993 1 04-04-2003 1 14-11-2014 1 08-02-2009 1 29-10-1991 1 01-02-1998 1 22-06-2009 1 09-05-2009 1 14-01-2005 1 21-04-2014 1 15-07-2000 1 30-09-1991 1 02-01-2004 1 04-12-2007 1 10-12-1993 1 11-05-1997 1 14-10-1992 1 02-10-2003 1 11-01-2010 1 25-01-2006 1 25-06-2002 1 20-11-2002 1 21-08-1991 1 03-06-2014 1 18-12-2013 1 12-10-1998 1 12-07-2013 1 29-06-2006 1 17-06-2005 1 05-10-1999 1 09-04-2001 1 13-10-1990 1 13-04-2006 1 02-06-2012 1 06-09-1995 1 11-09-1997 1 26-08-2004 1 08-12-2001 1 24-10-2004 1 24-09-1994 1 25-11-1994 1 01-10-2010 1 17-02-2003 1 16-12-2007 1 22-06-1990 1 10-02-1993 1 30-07-2003 1 17-07-1995 1 17-08-1994 1 27-09-2010 1 08-05-2001 1 23-05-1999 1 17-02-1995 1 13-09-2002 1 30-06-2004 1 04-04-1992 1 16-11-2004 1 18-07-2002 1 09-03-2007 1 08-01-1990 1 02-09-1996 1 06-05-2002 1 12-03-1994 1 11-12-1994 1 10-10-1993 1 10-11-1992 1 20-01-2014 1 16-09-1997 1 13-11-2014 1 12-04-2013 1 05-08-1993 1 06-08-1999 1 14-05-2006 1 28-10-1999 1 11-02-2003 1 17-09-2014 1 02-12-2011 1 25-02-2008 1 08-08-1994 1 25-05-2002 1 25-04-1991 1 06-05-1991 1 04-01-1996 1 18-02-2000 1 21-12-2006 1 22-05-1999 1 14-06-2004 1 18-10-1996 1 04-12-2005 1 27-07-2005 1 23-12-2013 1 04-05-1995 1 14-11-1999 1 29-05-2003 1 13-04-2002 1 02-10-1990 1 05-08-2012 1 28-04-2005 1 20-07-2008 1 15-03-1992 1 03-11-1991 1 18-03-2008 1 24-09-1992 1 01-12-2008 1 09-08-2002 1 09-02-2000 1 18-02-1995 1 07-06-1994 1 16-06-1993 1 05-07-2009 1 10-10-2005 1 15-06-2004 1 25-12-2001 1 05-08-2014 1 07-04-1994 1 30-12-2011 1 18-06-1998 1 10-09-1998 1 25-07-2011 1 03-10-2000 1 18-02-1999 1 25-08-2011 1 26-06-2001 1 16-03-2008 1 14-01-2008 1 03-02-1990 1 21-07-2008 1 10-04-1992 1 07-09-2006 1 09-08-2003 1 21-10-2009 1 23-07-2009 1 04-01-2009 1 18-08-1996 1 03-09-2013 1 04-02-1992 1 19-12-1998 1 08-08-2013 1 05-12-2013 1 14-01-1998 1 01-02-2011 1 07-11-2008 1 29-05-1995 1 15-11-2004 1 09-10-2012 1 26-01-1996 1 20-10-1993 1 25-06-2011 1 24-03-2007 1 22-11-2012 1 16-11-2000 1 17-08-1991 1 06-10-2012 1 22-03-1992 1 12-04-2002 1 07-04-2005 1 02-06-2002 1 21-05-2000 1 11-07-2007 1 17-02-2009 1 30-04-2007 1 19-12-1997 1 17-03-1990 1 11-11-1994 1 07-09-1999 1 16-02-2001 1 28-10-2008 1 30-03-1995 1 15-05-2000 1 23-01-1996 1 23-02-1990 1 20-08-1990 1 06-06-1993 1 18-02-2007 1 11-11-1996 1 03-07-1992 1 10-07-2012 1 03-03-2007 1 15-06-2014 1 27-06-2005 1 05-12-2009 1 08-01-2013 1 27-05-2003 1 21-02-1995 1 11-04-2005 1 23-01-2002 1 18-01-2003 1 29-01-1993 1 07-03-2008 1 26-01-1994 1 14-03-1990 1 19-04-2009 1 01-07-2005 1 08-07-1996 1 16-06-2003 1 20-09-1993 1 28-05-2014 1 19-04-2006 1 30-11-1996 1 25-05-2011 1 15-10-1996 1 21-03-1998 1 10-01-2004 1 09-01-1999 1 28-10-1997 1 28-03-2001 1 13-07-2013 1 09-12-2001 1 05-08-1997 1 03-11-2009 1 21-11-1991 1 06-03-2004 1 21-01-2002 1 02-08-1990 1 14-02-1996 1 28-01-2003 1 29-04-1990 1 12-10-2006 1 28-03-2003 1 11-03-2007 1 02-10-1992 1 04-11-1991 1 29-03-1995 1 27-06-2006 1 17-08-2011 1 22-01-2011 1 04-01-2003 1 24-05-2006 1 30-10-1997 1 14-10-2005 1 01-05-1999 1 19-06-1996 1 15-08-2000 1 07-04-1992 1 26-05-2002 1 05-05-1993 1 27-05-1994 1 22-11-2008 1 24-12-2006 1 05-08-2006 1 27-05-2002 1 30-04-2006 1 18-03-2003 1 27-11-1990 1 24-04-2012 1 21-12-1999 1 06-06-1992 1 06-05-2014 1 03-03-1992 1 12-09-2001 1 06-06-2011 1 15-07-2004 1 15-01-1992 1 21-12-1996 1 03-08-2010 1 07-01-2005 1 02-05-2007 1 31-08-2014 1 04-02-2004 1 11-06-2008 1 23-11-1997 1 19-08-1995 1 06-12-1993 1 08-02-1996 1 05-10-1991 1 26-09-1991 1 13-05-2001 1 10-05-2009 1 27-10-2001 1 08-05-1995 1 06-03-2003 1 01-07-2013 1 03-08-2009 1 27-07-1997 1 29-05-1992 1 07-12-1998 1 01-03-2010 1 26-02-2005 1 21-04-2006 1 26-08-2000 1 07-04-1998 1 31-10-1993 1 19-04-2007 1 12-10-1995 1 19-09-2004 1 20-12-1990 1 11-02-1991 1 13-11-1998 1 07-11-1994 1 19-06-2008 1 25-10-2012 1 04-03-2001 1 29-07-2012 1 17-01-2015 1 19-09-2012 1 14-07-2000 1 21-06-1994 1 13-03-2008 1 11-12-2009 1 23-10-2007 1 07-04-2014 1 22-02-1992 1 26-10-1996 1 12-12-1998 1 04-07-2007 1 19-05-1992 1 16-01-1996 1 27-01-2007 1 19-10-1992 1 24-08-2000 1 18-10-2000 1 01-06-2006 1 19-12-2004 1 27-11-2005 1 27-09-2011 1 11-07-2002 1 21-11-2006 1 02-04-1991 1 28-08-1995 1 04-07-2013 1 18-02-1990 1 08-04-2003 1 02-01-2001 1 12-12-1999 1 17-06-2006 1 05-01-2013 1 18-01-2014 1 17-12-2003 1 10-04-2013 1 05-03-2009 1 12-12-1991 1 31-03-2005 1 25-08-2012 1 22-10-2011 1 09-01-2002 1 14-11-2010 1 30-01-2005 1 28-02-2004 1 30-08-2000 1 05-08-1996 1 31-07-2011 1 09-12-2006 1 28-08-2005 1 22-02-1994 1 28-01-1998 1 01-11-1991 1 20-11-2005 1 08-03-1998 1 20-03-1992 1 15-08-2011 1 27-01-1991 1 20-08-1996 1 07-12-1992 1 14-01-1999 1 20-11-1997 1 28-10-2006 1 17-11-2004 1 07-10-1990 1 28-12-1998 1 05-07-2001 1 05-02-1994 1 04-03-2000 1 30-07-1992 1 13-02-1991 1 06-03-2010 1 03-06-2009 1 30-11-1993 1 08-07-1990 1 25-04-2002 1 01-09-1992 1 15-04-2004 1 11-03-2014 1 07-04-2010 1 16-05-2001 1 20-09-1999 1 24-01-2010 1 22-07-2004 1 25-11-2006 1 12-02-1998 1 21-08-1994 1 02-09-2011 1 01-05-2010 1 19-09-2009 1 19-10-2001 1 31-12-2003 1 11-02-1994 1 31-10-2013 1 02-02-1996 1 06-02-2009 1 16-11-2007 1 07-01-2011 1 27-08-2014 1 19-10-1999 1 07-07-1997 1 08-09-2009 1 10-09-2002 1 23-06-2012 1 20-07-2007 1 25-11-2002 1 08-03-2009 1 05-07-1999 1 17-05-1994 1 27-12-2000 1 15-02-1993 1 16-09-2009 1 26-10-2012 1 16-03-1998 1 24-06-2014 1 02-10-1997 1 24-09-2001 1 23-07-1992 1 30-06-2002 1 31-07-1999 1 07-12-2001 1 04-01-2002 1 28-11-1991 1 11-07-1996 1 17-06-2008 1 21-12-2013 1 10-09-2000 1 16-12-2011 1 03-10-2014 1 10-04-2009 1 28-07-2002 1 21-03-1997 1 06-09-2008 1 29-06-2002 1 07-08-1991 1 19-01-1996 1 24-04-2007 1 28-09-2011 1 12-10-1997 1 25-01-2008 1 08-06-2005 1 20-04-2004 1 27-02-2010 1 29-11-1999 1 23-08-2005 1 14-05-2001 1 03-04-2001 1 22-01-1993 1 05-01-2014 1 06-12-1991 1 29-04-2010 1 06-11-2013 1 29-02-1992 1 06-12-1995 1 09-08-1990 1 28-10-1998 1 07-11-1992 1 02-12-2012 1 13-04-2003 1 07-11-2012 1 13-12-2014 1 21-10-2005 1 19-06-1992 1 27-06-2008 1 11-10-2012 1 11-12-1998 1 02-11-2010 1 20-08-1994 1 03-11-1990 1 06-06-2014 1 01-05-1997 1 02-01-1991 1 01-01-2008 1 19-10-1990 1 11-08-1999 1 22-07-1999 1 30-08-2012 1 02-06-2010 1 21-02-1999 1 18-08-2006 1 18-01-1997 1 04-12-2013 1 24-05-2003 1 06-01-2011 1 05-01-1991 1 10-03-1991 1 16-08-2003 1 29-10-2006 1 04-08-1997 1 10-02-1997 1 26-08-2013 1 29-06-2009 1 15-08-1999 1 10-05-2012 1 27-06-1996 1 01-04-2002 1 14-11-2005 1 03-08-1993 1 23-03-2009 1 25-07-1995 1 16-03-2014 1 31-01-2011 1 16-06-1991 1 14-04-1993 1 15-07-2011 1 29-07-1995 1 16-09-2013 1 21-01-2009 1 25-04-2007 1 11-10-1994 1 20-04-2013 1 12-05-2007 1 02-02-2001 1 13-10-1991 1 03-09-1991 1 25-12-1997 1 14-02-1992 1 10-03-2004 1 29-09-2001 1 13-11-1995 1 22-04-2010 1 21-08-2010 1 24-10-1997 1 09-11-1991 1 22-02-2015 1 16-04-1995 1 18-06-2000 1 01-10-2001 1 24-07-2012 1 02-08-1992 1 17-03-2010 1 06-12-1996 1 10-08-2006 1 03-02-1994 1 03-02-1993 1 15-08-2002 1 05-12-2007 1 09-02-2007 1 08-02-1990 1 01-10-2006 1 16-03-2003 1 30-03-1999 1 14-02-1990 1 01-08-2010 1 20-01-1996 1 29-12-2010 1 30-04-2013 1 23-07-2014 1 09-11-1992 1 25-03-2007 1 16-06-2006 1 11-03-1996 1 29-10-1993 1 02-11-2012 1 03-06-1991 1 08-06-2002 1 18-06-2010 1 24-07-2007 1 18-06-1997 1 30-09-1993 1 28-03-1995 1 02-08-2006 1 28-08-1998 1 20-11-1991 1 25-12-1991 1 23-08-2009 1 24-07-1999 1 18-08-2007 1 18-02-2011 1 02-04-2002 1 05-05-1991 1 02-08-1991 1 10-04-1994 1 26-09-2010 1 29-02-2004 1 31-12-2012 1 25-02-2005 1 07-03-2005 1 01-11-2014 1 06-08-1994 1 12-08-2004 1 17-09-2003 1 27-12-2001 1 16-01-2013 1 24-12-2012 1 27-04-2012 1 29-11-2001 1 04-03-2014 1 21-08-2006 1 18-03-1990 1 03-04-2013 1 06-12-2006 1 23-07-1996 1 28-09-2007 1 20-11-2010 1 17-09-2011 1 20-11-2008 1 01-03-1991 1 13-08-2004 1 24-06-1998 1 30-08-1997 1 04-02-2011 1 17-04-2005 1 24-03-2006 1 28-03-2006 1 25-11-1991 1 13-01-1991 1 27-01-2000 1 08-10-1995 1 09-05-2008 1 20-02-2001 1 24-02-2012 1 13-12-1993 1 23-01-2003 1 22-07-2002 1 03-02-2008 1 04-06-1996 1 21-09-1990 1 13-12-1995 1 28-11-2002 1 18-05-1993 1 14-03-2012 1 28-01-1993 1 10-02-2002 1 05-11-2000 1 27-11-1992 1 18-10-2006 1 01-12-2000 1 03-02-2006 1 24-10-2007 1 19-09-2007 1 28-02-2010 1 11-10-1992 1 16-11-1991 1 04-03-2006 1 16-07-2014 1 28-02-1997 1 28-10-2007 1 17-07-2012 1 16-07-1991 1 03-08-1994 1 29-07-1996 1 28-09-1994 1 04-11-2004 1 28-07-2014 1 05-08-2005 1 29-07-2009 1 21-04-2001 1 10-02-2008 1 22-09-2002 1 04-11-2010 1 14-11-2007 1 01-06-1997 1 23-02-2014 1 24-05-2004 1 25-11-2004 1 10-02-1994 1 04-03-2003 1 15-02-2011 1 01-05-2013 1 03-01-2015 1 23-01-2013 1 11-09-2004 1 25-08-1990 1 26-06-2009 1 13-04-1991 1 28-12-1999 1 11-07-2001 1 09-11-2008 1 01-04-1994 1 21-04-2008 1 22-12-2012 1 20-05-1990 1 11-09-2010 1 17-04-1999 1 07-07-2013 1 06-02-2006 1 24-01-2009 1 30-06-2000 1 21-06-1997 1 29-03-2005 1 04-02-2013 1 16-05-1990 1 27-01-1990 1 28-12-2004 1 29-09-1997 1 07-05-1990 1 18-09-1999 1 08-05-2005 1 11-05-2010 1 02-08-2009 1 15-10-2005 1 26-01-2011 1 10-04-1996 1 23-04-1995 1 18-10-1995 1 16-12-1998 1 18-07-1991 1 14-09-2001 1 09-03-1999 1 24-06-2003 1 26-08-2012 1 01-03-1997 1 19-05-1993 1 06-07-1996 1 09-12-2005 1 22-09-1990 1 29-08-1999 1 06-11-2001 1 07-08-1994 1 11-02-2010 1 12-07-2006 1 06-10-2002 1 19-12-2011 1 10-06-2001 1 15-02-2001 1 23-08-1996 1 07-08-1992 1 05-07-2003 1 10-02-2007 1 28-12-2014 1 17-10-2014 1 21-07-1996 1 02-07-1991 1 07-01-2008 1 11-10-2003 1 13-02-2003 1 26-06-2000 1 16-07-2004 1 04-03-2007 1 09-10-2009 1 09-07-2013 1 14-03-2007 1 07-12-1993 1 12-02-2009 1 23-02-1993 1 28-01-1992 1 16-07-2003 1 24-06-2009 1 04-07-1997 1 18-04-1993 1 21-03-2008 1 03-02-1999 1 06-09-2000 1 11-11-2013 1 09-10-1995 1 25-10-2007 1 09-10-1990 1 20-03-1991 1 19-12-2001 1 08-11-2009 2 27-07-2014 2 25-12-2013 2 11-03-2010 2 15-05-1997 2 16-05-2008 2 28-12-2002 2 16-07-2002 2 20-07-1991 2 07-12-1995 2 04-06-2000 2 03-02-1997 2 28-01-2010 2 21-09-1996 2 09-08-2004 2 14-12-1991 2 04-05-2000 2 05-01-1992 2 19-09-1995 2 03-01-2004 2 07-04-1999 2 30-08-1993 2 28-12-1991 2 09-07-2002 2 29-09-1999 2 07-12-1999 2 21-09-2005 2 25-09-2001 2 05-07-2014 2 11-11-1998 2 15-11-1997 2 09-03-2003 2 20-09-1990 2 25-05-1990 2 07-07-1996 2 24-06-1990 2 21-12-2002 2 06-05-2007 2 14-07-1997 2 07-11-1997 2 22-08-1991 2 14-04-1992 2 29-01-1998 2 05-08-1992 3 28-04-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INSURED_RELATIONSHIP : 6 unmarried 141 wife 155 husband 170 not-in-family 174 other-relative 177 own-child 183 Name: insured_relationship, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 07-02-2015 10 25-01-2015 10 11-02-2015 10 10-02-2015 10 19-02-2015 10 29-01-2015 11 26-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 27-01-2015 13 23-01-2015 13 03-02-2015 13 09-02-2015 13 20-02-2015 14 27-02-2015 14 22-01-2015 14 15-01-2015 15 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-02-2015 16 16-01-2015 16 16-02-2015 16 05-02-2015 16 13-02-2015 16 06-01-2015 17 09-01-2015 17 24-02-2015 17 08-02-2015 17 26-02-2015 17 20-01-2015 18 28-02-2015 18 14-02-2015 18 18-01-2015 18 03-01-2015 18 25-02-2015 18 01-02-2015 18 14-01-2015 19 01-01-2015 19 21-01-2015 19 12-01-2015 19 23-02-2015 19 21-02-2015 19 31-01-2015 20 12-02-2015 20 22-02-2015 20 06-02-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 10-01-2015 24 04-02-2015 24 24-01-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_TYPE : 4 Parked Car 84 Vehicle Theft 94 Single Vehicle Collision 403 Multi-vehicle Collision 419 Name: incident_type, dtype: int64 COLLISION_TYPE : 4 Unknown 178 Front Collision 254 Side Collision 276 Rear Collision 292 Name: collision_type, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 3340 3rd Hwy 1 3457 Texas Lane 1 9103 MLK Lane 1 1507 Solo Ave 1 4653 Pine St 1 6684 Solo Lane 1 9580 MLK Ave 1 3006 Lincoln Ridge 1 1358 Maple St 1 3029 5th Ave 1 2217 Tree Lane 1 4335 1st St 1 5189 Francis Drive 1 1553 Lincoln St 1 3660 Andromedia Hwy 1 5812 Weaver Ave 1 5499 Elm Hwy 1 4567 Pine Ave 1 7511 1st Ave 1 4710 Lincoln Hwy 1 5778 Pine Ridge 1 4143 Maple Ridge 1 4175 Elm Ridge 1 8281 Lincoln Lane 1 1725 Solo Lane 1 3929 Elm Ave 1 9787 Andromedia Ave 1 5894 Flute Drive 1 2100 Francis Drive 1 1298 Maple Hwy 1 2774 Apache Drive 1 8617 Best Ave 1 9720 Lincoln Hwy 1 3495 Britain Drive 1 6359 MLK Ridge 1 1331 Elm Ridge 1 6655 5th Drive 1 9988 Rock Ridge 1 6582 Elm Lane 1 2290 4th Ave 1 1529 Elm Ridge 1 5678 Lincoln Drive 1 9397 5th Hwy 1 1562 Britain St 1 9439 MLK St 1 7240 5th Ridge 1 5790 Flute Ridge 1 2873 Flute Ave 1 3592 MLK Ridge 1 3221 Solo Ridge 1 4239 Weaver Ave 1 7523 Oak Lane 1 2889 Francis St 1 9523 Solo Hwy 1 6375 2nd Lane 1 7314 Tree Drive 1 9278 Francis Ridge 1 5201 Texas Hwy 1 7601 Andromedia Lane 1 2644 Elm Drive 1 4972 Francis Lane 1 2123 MLK Ridge 1 2204 Washington Lane 1 9239 Washington Ridge 1 7846 Andromedia Drive 1 9760 Solo Lane 1 2577 Texas Ridge 1 8876 1st St 1 5028 Maple Ridge 1 9105 Tree Lane 1 2654 Embaracadero St 1 6834 1st Drive 1 3553 Texas Ave 1 6399 Oak Drive 1 6179 3rd Ridge 1 5821 2nd St 1 7628 4th Lane 1 7488 Lincoln Lane 1 4857 Weaver St 1 2920 5th Ave 1 8118 Elm Ridge 1 8576 Andromedia St 1 5104 Francis Drive 1 3929 Oak Drive 1 1989 Solo Lane 1 8864 Tree Ridge 1 6250 1st Ridge 1 2654 Elm Drive 1 1655 Francis Hwy 1 3926 Rock Lane 1 4642 Rock Ridge 1 9214 Texas Drive 1 6149 Best Ridge 1 4814 Lincoln Lane 1 5071 Flute Ridge 1 1833 Solo Ave 1 4232 Britain Ridge 1 5783 Oak Ave 1 4939 Best St 1 6956 Maple Drive 1 2968 Andromedia Ave 1 1364 Best St 1 6618 Cherokee Drive 1 6092 5th Ave 1 5474 Weaver Hwy 1 1580 Maple Lane 1 8269 Sky Hwy 1 2324 Texas Ridge 1 4546 Tree St 1 4964 Elm Lane 1 5994 5th Ave 1 8014 Embaracadero Drive 1 4154 Lincoln Hwy 1 3966 Francis Ridge 1 5838 Pine Lane 1 6678 Weaver Drive 1 3884 Pine Lane 1 8211 Sky Hwy 1 9418 5th Hwy 1 5071 1st Lane 1 6378 Britain Ave 1 5971 5th Hwy 1 1173 Andromedia Ave 1 6158 Sky Ridge 1 4633 5th Lane 1 1437 3rd Lane 1 7900 Sky Hwy 1 4699 Texas Ridge 1 8080 Oak Lane 1 9214 Elm Ridge 1 8214 Flute St 1 1679 2nd Hwy 1 2272 Embaracadero Drive 1 1328 Texas Lane 1 2275 Best Lane 1 8100 3rd Ave 1 3567 4th Drive 1 3998 Flute St 1 3540 Maple St 1 6309 Cherokee Ave 1 3311 2nd Drive 1 8336 1st Ridge 1 3796 Cherokee Drive 1 6550 Andromedia St 1 6751 5th Hwy 1 8983 Tree St 1 3416 Washington Drive 1 9038 2nd Lane 1 3087 Oak Hwy 1 7756 Solo Drive 1 1824 5th Lane 1 1316 Britain Ridge 1 7583 Washington Ave 1 7098 Lincoln Hwy 1 1267 Francis Hwy 1 7825 1st Ridge 1 7721 Washington Ridge 1 5279 Pine Ridge 1 8492 Andromedia Ridge 1 6957 Weaver Drive 1 3618 Sky Ave 1 6408 Weaver Ridge 1 7630 Rock Drive 1 7253 MLK St 1 4866 4th Hwy 1 3770 Flute Drive 1 6981 Weaver St 1 4492 Andromedia Ave 1 6440 Rock Lane 1 1353 Washington St 1 4486 Cherokee Ridge 1 7575 Pine St 1 3220 Rock Drive 1 2725 Britain Ridge 1 6409 Cherokee Drive 1 7649 Texas St 1 9818 Cherokee Ave 1 8538 Texas Lane 1 6206 3rd Ridge 1 3184 Oak Ave 1 9685 Sky Ridge 1 5771 Best St 1 7434 Oak Hwy 1 7644 Tree Ridge 1 2526 Embaracadero Ave 1 7061 Cherokee Drive 1 1102 Apache Hwy 1 2862 Tree Ridge 1 6723 Best Drive 1 1248 MLK Ridge 1 3982 Washington Hwy 1 5806 Embaracadero St 1 9067 Texas Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 6619 Flute Ave 1 7295 Tree Hwy 1 3320 5th Hwy 1 2603 Andromedia Hwy 1 6874 Maple Ridge 1 4862 Lincoln Hwy 1 3419 Apache St 1 5608 Solo St 1 9240 Britain Ave 1 1416 Cherokee Ridge 1 4672 MLK St 1 9821 Francis Ave 1 2193 4th Ridge 1 8689 Maple Hwy 1 7859 4th Ridge 1 7928 Maple Ridge 1 5211 Weaver Drive 1 4907 Andromedia Drive 1 5639 1st Ridge 1 9706 MLK Lane 1 4538 Flute Hwy 1 9633 MLK Lane 1 7709 Rock Lane 1 6939 3rd Hwy 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 2457 Washington Ave 1 7773 Tree Hwy 1 9373 Pine Hwy 1 3492 Flute Lane 1 7082 Oak Ridge 1 8822 Sky St 1 9911 Britain Lane 1 9728 Britain Hwy 1 6058 Andromedia Hwy 1 4460 4th Lane 1 5719 2nd Lane 1 9177 Texas Ave 1 9730 2nd Hwy 1 6985 Maple Lane 1 6467 Best Ave 1 6838 Flute Lane 1 1810 Elm Hwy 1 2500 Tree St 1 6971 Best Ridge 1 6859 Flute Ridge 1 8404 Embaracadero St 1 9633 4th St 1 2469 Francis Lane 1 2492 Lincoln Lane 1 1830 Sky St 1 2220 1st Lane 1 6317 Best St 1 6848 Elm Hwy 1 8917 Tree Ridge 1 2950 MLK Ave 1 7576 Pine Ridge 1 9744 Texas Drive 1 1110 4th Drive 1 5312 Francis Ridge 1 6530 Weaver Ave 1 4955 Lincoln Ridge 1 1365 Francis Ave 1 4237 4th St 1 6604 Apache Drive 1 7797 Tree Ridge 1 8758 5th St 1 8907 Tree Ave 1 2798 1st Ave 1 9417 Tree Hwy 1 2333 Maple Lane 1 4618 Flute Ave 1 8906 Elm Lane 1 4755 Best Lane 1 5846 Weaver Drive 1 2005 Texas Hwy 1 6259 Weaver St 1 1491 Francis Ridge 1 2381 1st Hwy 1 4614 MLK Ave 1 9360 3rd Drive 1 2037 5th Drive 1 2352 Sky Drive 1 7877 3rd Ridge 1 8742 4th St 1 6738 Washington Hwy 1 5780 4th Ave 1 3039 Oak Hwy 1 7897 Lincoln St 1 8456 1st Ave 1 6706 Francis Drive 1 1320 Flute Lane 1 1840 Embaracadero Ave 1 9138 1st St 1 8212 Flute Ridge 1 8548 Cherokee Ridge 1 7791 Britain Ridge 1 9935 4th Drive 1 6355 4th Hwy 1 9588 Solo St 1 6068 2nd St 1 3167 4th Ridge 1 5333 MLK Lane 1 3028 5th St 1 8493 Apache Drive 1 9070 Tree Ave 1 1956 Apache St 1 8198 Embaracadero Lane 1 1331 Britain Hwy 1 8782 3rd St 1 9737 Solo Hwy 1 5418 Britain Ave 1 5663 Oak Lane 1 3841 Washington Lane 1 6934 Lincoln Ave 1 2533 Elm St 1 3915 Embaracadero St 1 3834 Pine St 1 5475 Rock Lane 1 6889 Cherokee St 1 7466 MLK Ridge 1 4872 Rock Ridge 1 8834 Elm Drive 1 5341 5th Ave 1 5904 1st Drive 1 2376 Sky Ridge 1 7108 Tree St 1 7380 5th Hwy 1 3936 Tree Drive 1 6451 1st Hwy 1 5650 Sky Drive 1 6668 Andromedia Ridge 1 4625 MLK Drive 1 1469 Lincoln Drive 1 3998 4th Hwy 1 1951 Best Ave 1 8368 Cherokee Ave 1 8624 Francis Ave 1 4394 Oak St 1 8991 Texas Hwy 1 7930 Texas Ave 1 2617 Andromedia Drive 1 8006 Maple Hwy 1 9639 Britain Ridge 1 2820 Britain St 1 4519 Embaracadero St 1 7327 Lincoln Drive 1 2123 Texas Ave 1 8453 Elm St 1 3753 Francis Lane 1 6137 MLK St 1 8949 Rock Hwy 1 4475 Lincoln Ridge 1 1546 Cherokee Ave 1 7571 Elm Ridge 1 5168 5th Ave 1 7973 4th St 1 4119 Texas St 1 3797 Solo Lane 1 4885 Oak Lane 1 1919 4th Lane 1 9082 3rd Lane 1 4434 Weaver St 1 1515 Embaracadero St 1 7042 Maple Ridge 1 3952 Andromedia Lane 1 3925 Sky St 1 2299 1st St 1 6751 Pine Ridge 1 8203 Lincoln Ave 1 3697 Apache Drive 1 3818 Texas Ridge 1 1123 5th Lane 1 4242 Rock Lane 1 4702 Texas Drive 1 3656 Solo Ave 1 9286 Oak Ave 1 8701 5th Lane 1 4981 Weaver St 1 8306 1st Drive 1 7745 Washington Ridge 1 1386 Britain St 1 5969 Francis St 1 7168 Andromedia Ridge 1 1472 4th Drive 1 6676 Tree Lane 1 8941 Solo Ridge 1 1186 Rock St 1 2537 5th Ave 1 2804 Best St 1 6581 Rock Ridge 1 1275 4th Ridge 1 9942 Tree Ave 1 3196 Cherokee St 1 2318 Washington Hwy 1 3376 5th Drive 1 6851 3rd Drive 1 2865 Maple Lane 1 4347 2nd Ridge 1 9682 Cherokee Ridge 1 1213 4th Lane 1 9352 Washington Ave 1 6256 Elm St 1 5226 Maple St 1 1910 Sky Ave 1 1815 Cherokee Drive 1 4574 Britain Hwy 1 2280 4th Ave 1 6888 Elm Ridge 1 7397 4th Drive 1 2757 4th Hwy 1 8078 Britain Hwy 1 3127 Flute St 1 1346 5th Lane 1 1532 Washington St 1 6501 5th Drive 1 8940 Elm Ave 1 5649 Texas Ave 1 9719 4th Lane 1 1273 Rock Lane 1 3842 Solo Ridge 1 8766 Lincoln Lane 1 7236 Apache Lane 1 7544 Washington Ave 1 8096 Apache Hwy 1 3525 3rd Hwy 1 4826 5th St 1 9224 Sky Drive 1 4020 Best Drive 1 3809 Texas Lane 1 7495 Washington Ave 1 9020 Elm Ave 1 2808 Elm St 1 6492 4th Lane 1 2003 Maple Hwy 1 3618 Maple Lane 1 9657 5th Ave 1 8805 Cherokee Drive 1 3061 Francis Hwy 1 8621 Best Ridge 1 6605 Tree Ave 1 9573 Weaver Ave 1 8101 3rd Ridge 1 7460 Apache Lane 1 5318 5th Ave 1 8638 3rd Ave 1 7693 Cherokee Lane 1 3066 Francis Ave 1 4835 Britain Ridge 1 4629 Elm Ridge 1 1705 Weaver St 1 6309 5th Ave 1 2397 Cherokee Ave 1 6658 Weaver St 1 9742 5th Ridge 1 9034 Weaver Ridge 1 8381 Solo Hwy 1 9316 Pine Ave 1 5630 1st Drive 1 5997 Embaracadero Drive 1 8832 Pine Drive 1 6677 Andromedia Drive 1 4994 Lincoln Drive 1 9847 Elm St 1 5602 Britain St 1 1809 Sky St 1 1555 Washington Lane 1 1916 Elm St 1 7782 Rock St 1 5532 Francis Lane 1 6741 Oak Ridge 1 7693 Britain Lane 1 1128 Maple Lane 1 5280 Pine Ave 1 5532 Weaver Ridge 1 9816 Britain St 1 8204 Pine Lane 1 4627 Elm Ridge 1 3092 Texas Drive 1 1914 Francis St 1 5925 Tree Hwy 1 3397 5th Ave 1 7281 Oak St 1 4447 Francis Hwy 1 1845 Best St 1 1325 1st Lane 1 2697 Oak Drive 1 9724 Maple St 1 7121 Britain Drive 1 8542 Lincoln Ridge 1 5061 Francis Ave 1 2777 Solo Drive 1 6493 Lincoln Lane 1 1953 Sky Lane 1 1618 Maple Hwy 1 1371 Texas Lane 1 6193 1st Hwy 1 3653 Elm Drive 1 4429 Washington St 1 3707 Oak Ridge 1 3664 Francis Ridge 1 8667 Weaver Lane 1 7477 MLK Drive 1 2903 Weaver Drive 1 7582 Pine Drive 1 4369 Maple Lane 1 5191 4th St 1 4585 Francis Ave 1 4176 Britain Hwy 1 3847 Elm Hwy 1 2832 Andromedia Lane 1 6931 Elm St 1 4755 1st St 1 2199 Texas Drive 1 7976 Britain Drive 1 8489 Pine Hwy 1 8983 Francis Ridge 1 4876 Washington Drive 1 7705 Lincoln Drive 1 6516 Solo Drive 1 4214 MLK Ridge 1 4231 3rd Ave 1 1738 Solo Lane 1 5743 4th Ridge 1 8212 Rock Ave 1 2823 Weaver Lane 1 3052 Weaver Ridge 1 7783 Lincoln Hwy 1 6515 Oak Lane 1 5779 2nd Lane 1 8021 Flute Ave 1 3414 Elm Ave 1 3995 Lincoln Hwy 1 7733 Britain Lane 1 9322 Rock Hwy 1 9325 Lincoln Drive 1 1558 1st Ridge 1 3275 Pine St 1 6428 Andromedia Lane 1 2711 Britain Ave 1 9573 2nd Ave 1 3439 Andromedia Hwy 1 7909 Andromedia Hwy 1 7684 Francis Ridge 1 8509 Apache St 1 4554 Sky Ave 1 6435 Texas Ave 1 8845 5th Ave 1 3288 Tree Lane 1 8926 Texas Ridge 1 3799 Embaracadero Drive 1 2756 Britain Hwy 1 5874 1st Hwy 1 7331 Sky Hwy 1 8821 Elm St 1 9422 Washington Ridge 1 2986 MLK Drive 1 9633 Rock Hwy 1 2651 MLK Lane 1 3263 Pine Ridge 1 6583 MLK Ridge 1 6608 Apache Lane 1 1699 Oak Drive 1 6315 2nd Lane 1 1012 5th Lane 1 8954 Apache Lane 1 3102 Apache St 1 3903 Oak Ave 1 4188 Britain Ave 1 3246 Britain Ridge 1 7780 Flute Lane 1 4577 Sky Hwy 1 2430 MLK Ave 1 7281 Maple Hwy 1 8655 Cherokee Lane 1 5584 Britain Lane 1 8964 Francis St 1 8188 Tree Ave 1 6303 1st Drive 1 3693 Pine Ave 1 9878 Washington Ave 1 6011 Britain St 1 6456 Andromedia Drive 1 2311 4th St 1 8215 Flute Drive 1 8639 5th Hwy 1 3751 Tree Hwy 1 6724 Andromedia St 1 7756 Pine Hwy 1 3486 Flute Ave 1 4780 Best Drive 1 5431 3rd Ridge 1 7740 MLK St 1 5459 MLK Ave 1 1240 Tree Lane 1 9489 3rd St 1 7002 Oak Hwy 1 1628 Best Drive 1 6045 Andromedia St 1 1220 MLK Ave 1 7316 Texas Ave 1 5985 Lincoln Lane 1 6128 Elm Lane 1 9153 3rd Hwy 1 8862 Maple Ridge 1 8081 Flute Ridge 1 4098 Weaver Ridge 1 3089 Oak Ridge 1 5771 Sky Ave 1 6603 Francis Hwy 1 8920 Best Ave 1 8492 Weaver Hwy 1 3327 Lincoln Drive 1 4264 Lincoln Ridge 1 1992 Britain Drive 1 1762 Maple Hwy 1 4279 Solo Drive 1 4431 Rock St 1 5839 Weaver Lane 1 2900 Sky Drive 1 2914 Oak Drive 1 1589 Pine St 1 1880 Weaver Drive 1 3900 Texas St 1 4793 4th Ridge 1 9154 MLK Hwy 1 3104 Sky Drive 1 4558 3rd Hwy 1 5051 Elm St 1 1536 Flute Drive 1 5058 4th Lane 1 6681 Texas Ridge 1 4693 Lincoln Hwy 1 6494 4th Ave 1 1269 Flute Drive 1 7574 4th St 1 8879 1st Drive 1 3097 4th Drive 1 6110 Rock Ridge 1 4985 Sky Lane 1 4477 5th Ave 1 2293 Washington Ave 1 4782 Sky Lane 1 7717 Britain Hwy 1 4937 Flute Drive 1 1941 5th Ridge 1 8085 Andromedia St 1 5914 Oak Ave 1 5074 3rd St 1 5383 Maple Drive 1 4053 Sky Lane 1 6853 Sky Hwy 1 5007 Oak St 1 9875 MLK Ave 1 4910 1st Lane 1 7609 Rock St 1 1215 Pine Hwy 1 7299 Apache St 1 6574 4th Drive 1 7877 Sky Lane 1 2078 3rd Ave 1 4390 4th Drive 1 9751 Sky Ridge 1 5765 Washington St 1 5480 3rd Ridge 1 6738 Francis Hwy 1 1388 Embaracadero Hwy 1 3492 Britain St 1 6955 Pine Drive 1 5178 Weaver Hwy 1 5812 3rd Hwy 1 9138 3rd St 1 9317 Apache Ave 1 3878 Tree Lane 1 9748 Sky Drive 1 2644 MLK Drive 1 4905 Best Lane 1 9734 2nd Ridge 1 5622 Best Ridge 1 9751 Tree St 1 7459 Flute St 1 4158 Washington Lane 1 5249 4th Ave 1 3808 5th Ave 1 6044 Weaver Drive 1 7223 Embaracadero St 1 3846 4th Hwy 1 3790 Andromedia Hwy 1 3508 Washington St 1 6719 Flute St 1 9562 4th Ridge 1 3706 4th Hwy 1 3098 Oak Lane 1 9910 Maple Ave 1 1512 Rock Lane 1 3422 Flute St 1 1643 Washington Hwy 1 5621 4th Ave 1 2509 Rock Drive 1 5586 2nd St 1 6634 Texas Ridge 1 9369 Flute Hwy 1 3423 Francis Ave 1 3172 Tree Ridge 1 1030 Pine Lane 1 7142 5th Lane 1 1832 Elm Hwy 1 5093 Flute Lane 1 3171 Andromedia Lane 1 7178 Best Drive 1 8150 Washington Ridge 1 5540 Sky St 1 5585 Washington Drive 1 7121 Rock St 1 1879 4th Lane 1 7066 Texas Ave 1 2886 Tree Ridge 1 9798 Sky Ridge 1 1589 Best Ave 1 3659 Oak Lane 1 4981 Flute Hwy 1 5260 Francis Drive 1 9358 Texas Ridge 1 3289 Britain Drive 1 7819 2nd Ave 1 6067 Weaver Ridge 1 4687 5th Drive 1 3323 1st Lane 1 5160 2nd Hwy 1 4931 Maple Drive 1 7393 Washington St 1 7502 Rock Lane 1 9397 Francis St 1 1923 2nd Hwy 1 3814 Britain Drive 1 3100 Best St 1 1929 Britain Drive 1 4095 MLK St 1 3966 Oak Hwy 1 8973 Washington St 1 5455 Oak Hwy 1 8995 1st Ave 1 5812 Oak St 1 7069 4th Hwy 1 2577 Washington Drive 1 2494 Andromedia Drive 1 8049 4th St 1 1681 Cherokee Hwy 1 4254 Best Ridge 1 5782 Rock Drive 1 7447 Lincoln Ridge 1 4983 MLK Ridge 1 7041 Tree Ridge 1 5640 Embaracadero Lane 1 5380 Pine St 1 7816 MLK Lane 1 7701 Tree St 1 1707 Sky Ave 1 2087 Apache Ave 1 7144 Andromedia St 1 6390 Apache St 1 4539 Texas St 1 5865 Sky Lane 1 4055 2nd Drive 1 2849 Pine Drive 1 2889 Weaver St 1 5277 Texas Lane 1 8668 Flute St 1 8618 Texas Lane 1 3930 Embaracadero St 1 2905 Embaracadero Drive 1 9236 2nd Hwy 1 6443 Washington Ridge 1 9603 Texas Lane 1 6717 Best Drive 1 8167 Apache Ave 1 1376 Pine St 1 6731 Andromedia Hwy 1 4721 Cherokee Hwy 1 2253 Maple Ave 1 9918 Andromedia Drive 1 5276 2nd Lane 1 9007 Francis Hwy 1 5352 Lincoln Drive 1 8245 4th Hwy 1 6660 MLK Drive 1 4545 4th Ridge 1 9101 2nd Hwy 1 8770 1st Lane 1 1957 Washington Ave 1 6608 MLK Hwy 1 7121 Francis Lane 1 6357 Texas Lane 1 4615 Embaracadero Ave 1 7552 3rd St 1 4296 Pine Hwy 1 1218 Sky Hwy 1 3769 Sky St 1 7954 Tree Ridge 1 5862 Apache Ridge 1 3771 4th St 1 1578 5th Lane 1 1985 5th Ave 1 8097 Maple Lane 1 7428 Sky Hwy 1 8602 Washington Ridge 1 8353 Britain Ridge 1 3122 Apache Drive 1 6638 Tree Drive 1 8125 Texas Ridge 1 4414 Solo Drive 1 3777 Maple Ave 1 4268 2nd Ave 1 1229 5th Ave 1 7238 2nd St 1 3227 Maple Ave 1 1306 Andromedia St 1 5901 Elm Drive 1 6702 Andromedia St 1 1381 Francis Ave 1 2299 Britain Drive 1 6945 Texas Hwy 1 8718 Apache Lane 1 3658 Rock Drive 1 6035 Rock Ave 1 7476 4th St 1 3041 3rd Ave 1 9245 Weaver Ridge 1 4107 MLK Ridge 1 6522 Apache Drive 1 6117 4th Ave 1 7923 Elm Ave 1 1741 Best Ridge 1 1806 Weaver Ridge 1 3488 Flute Lane 1 2539 Embaracadero Ridge 1 1133 Apache St 1 1654 Pine St 1 5769 Texas Lane 1 8704 Britain Lane 1 5868 Best Drive 1 6104 Oak Ave 1 2668 Cherokee St 1 9488 Best Drive 1 9856 Apache St 1 7629 5th St 1 6331 MLK Ave 1 8579 Apache Drive 1 2289 Weaver Ridge 1 6429 4th Hwy 1 9081 Cherokee Hwy 1 7535 5th Lane 1 8477 Francis Hwy 1 7201 Washington Ave 1 2230 1st St 1 9610 Cherokee St 1 3743 Andromedia Ridge 1 6479 Francis Ave 1 3418 Texas Lane 1 2086 Francis Drive 1 7197 2nd Drive 1 8459 Apache Ave 1 9043 Maple Hwy 1 9760 4th Hwy 1 4291 Sky Hwy 1 9078 Francis Ridge 1 2003 2nd Hwy 1 8586 1st Ridge 1 1091 1st Drive 1 3053 Lincoln Drive 1 9148 4th Hwy 1 2100 MLK St 1 8809 Flute St 1 1620 Oak Ave 1 7529 Solo Ridge 1 5269 Flute Hwy 1 2337 Lincoln Hwy 1 4453 Best Ave 1 5650 Rock Ave 1 5236 Weaver Drive 1 2878 Britain Hwy 1 2014 Rock Ave 1 9066 Best Ridge 1 1818 Tree St 1 5868 Sky Hwy 1 8064 4th Ave 1 3555 Francis Ridge 1 9611 Pine Ridge 1 4671 5th Ridge 1 6329 Apache Ave 1 9929 Rock Drive 1 4234 Cherokee Lane 1 2646 MLK Drive 1 9484 Pine Drive 1 7500 Texas Ridge 1 3177 MLK Ridge 1 4116 Embaracadero Lane 1 7162 Maple Ave 1 8749 Tree St 1 7570 Cherokee Drive 1 4496 Pine Lane 1 3639 Flute Hwy 1 1028 Sky Lane 1 1454 5th Ridge 1 8957 Weaver Drive 1 1087 Flute Drive 1 6484 Tree Drive 1 3447 Solo Ave 1 2306 5th Lane 1 8811 Maple Hwy 1 7615 Weaver Drive 1 7405 Oak St 1 8524 Pine Lane 1 9529 4th Drive 1 8897 Sky St 1 7504 Flute Drive 1 9293 Pine Lane 1 3550 Washington Ave 1 2753 Cherokee Ave 1 7155 Apache Drive 1 4939 Oak Lane 1 2980 Sky Ridge 1 7551 Britain Lane 1 3805 Lincoln Hwy 1 2787 MLK St 1 5924 Maple Drive 1 6165 Rock Ridge 1 3167 2nd St 1 6536 MLK Hwy 1 1540 Apache Lane 1 7835 Cherokee Hwy 1 3094 Best Lane 1 3982 Weaver Lane 1 4995 Weaver Ridge 1 9980 Lincoln Ave 1 3590 Best Hwy 1 3443 Maple Ridge 1 2809 Francis Lane 1 2063 Weaver St 1 5455 Tree Ridge 1 7135 Flute Lane 1 6384 5th Ridge 1 5499 Flute Ridge 1 6260 5th Lane 1 9109 Britain Drive 1 8442 Britain Hwy 1 7705 Best Ridge 1 2850 Washington St 1 5445 Tree Hwy 1 1126 Texas Hwy 1 7534 MLK Hwy 1 8872 Oak Ridge 1 1515 Pine Lane 1 9169 Pine Ridge 1 6259 Lincoln Hwy 1 8946 2nd Drive 1 5363 Weaver Lane 1 5224 5th Lane 1 4272 Oak Ridge 1 1422 Flute Ave 1 7828 Cherokee Ave 1 3726 MLK Hwy 1 1617 Rock Drive 1 3110 Lincoln Lane 1 9279 Oak Hwy 1 3835 5th Ave 1 4434 Lincoln Ave 1 3654 Cherokee Ave 1 9169 Cherokee Hwy 1 6191 Oak Lane 1 3282 4th Lane 1 4058 Tree Drive 1 4652 Flute Drive 1 2201 4th Lane 1 8991 Embaracadero Ave 1 1687 3rd Lane 1 5022 1st St 1 5855 Apache St 1 3872 5th Drive 1 7819 Oak St 1 7426 Rock Drive 1 7112 Weaver Ave 1 2117 Lincoln Hwy 1 1135 Solo Lane 1 5053 Tree Drive 1 5506 Best St 1 2696 Cherokee Ridge 1 6770 1st St 1 9794 Embaracadero St 1 4905 Francis Ave 1 9879 Apache Drive 1 5483 Francis Drive 1 2048 3rd Ridge 1 2733 Texas Drive 1 8233 Tree Drive 1 2215 Best Ave 1 2942 1st Lane 1 4965 MLK Drive 1 5124 Maple St 1 1598 3rd Drive 1 5667 4th Drive 1 7785 Lincoln Lane 1 2352 MLK Drive 1 3706 Texas Hwy 1 8917 Cherokee Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.insured_relationship.value_counts()
own-child 183 other-relative 177 not-in-family 174 husband 170 wife 155 unmarried 141 Name: insured_relationship, dtype: int64
fraud_con['insured_relationship'] = fraud_con['insured_relationship'].astype('category')
fraud_con['insured_relationship'] = fraud_con['insured_relationship'].cat.codes
fraud_con.insured_relationship.value_counts()
3 183 2 177 1 174 0 170 5 155 4 141 Name: insured_relationship, dtype: int64
fraud_con.collision_type.value_counts()
Rear Collision 292 Side Collision 276 Front Collision 254 Unknown 178 Name: collision_type, dtype: int64
fraud_con['collision_type'] = fraud_con['collision_type'].astype('category')
fraud_con['collision_type'] = fraud_con['collision_type'].cat.codes
fraud_con.collision_type.value_counts()
1 292 2 276 0 254 3 178 Name: collision_type, dtype: int64
fraud_con.incident_type.value_counts()
Multi-vehicle Collision 419 Single Vehicle Collision 403 Vehicle Theft 94 Parked Car 84 Name: incident_type, dtype: int64
fraud_con['incident_type'] = fraud_con['incident_type'].astype('category')
fraud_con['incident_type'] = fraud_con['incident_type'].cat.codes
fraud_con.incident_type.value_counts()
0 419 2 403 3 94 1 84 Name: incident_type, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 06-09-2005 1 30-04-2002 1 19-05-2014 1 21-05-2010 1 09-08-1993 1 12-10-2002 1 20-06-2002 1 28-04-2013 1 15-04-2013 1 01-03-2012 1 19-01-1998 1 28-07-2008 1 04-12-1995 1 20-02-2009 1 28-04-1994 1 24-05-2001 1 19-09-1991 1 20-07-1990 1 13-06-1994 1 10-01-2012 1 08-03-1995 1 10-02-1998 1 19-07-2006 1 11-01-2009 1 12-01-2012 1 03-09-2008 1 11-07-1991 1 29-08-2010 1 09-06-2001 1 05-07-2007 1 06-06-2002 1 05-07-2000 1 26-11-2001 1 05-02-2006 1 27-04-2009 1 08-07-2001 1 05-06-2011 1 23-04-2008 1 11-08-2007 1 15-11-2000 1 17-02-2010 1 01-03-2002 1 03-05-1991 1 17-11-2009 1 05-05-1998 1 10-12-2014 1 13-06-1992 1 29-05-1999 1 05-12-2014 1 25-04-2014 1 03-12-2005 1 05-12-1995 1 21-02-2006 1 27-10-2013 1 16-10-2002 1 12-10-1994 1 20-02-1998 1 05-02-1991 1 18-10-2005 1 18-11-2011 1 27-02-1994 1 22-03-2000 1 08-01-1994 1 08-06-1994 1 18-12-1994 1 19-03-1992 1 29-08-1995 1 06-06-2001 1 12-01-2010 1 29-03-2004 1 13-09-2003 1 08-04-1991 1 26-03-1995 1 15-07-1990 1 12-11-2009 1 01-02-1990 1 11-05-1996 1 23-06-2000 1 17-06-2009 1 24-04-1995 1 27-09-1990 1 03-03-1997 1 05-11-2008 1 02-09-2014 1 21-04-2003 1 03-11-1996 1 17-08-2014 1 15-09-1990 1 31-10-1997 1 03-10-1994 1 30-12-2002 1 06-03-2005 1 20-05-1995 1 16-07-1998 1 11-12-1992 1 09-04-1999 1 19-03-2014 1 15-12-1996 1 25-01-1999 1 08-02-1991 1 05-09-1996 1 10-11-2005 1 10-06-2014 1 18-11-1994 1 05-02-1997 1 30-08-2014 1 25-11-2009 1 12-09-1994 1 23-10-1996 1 13-11-2002 1 19-05-1990 1 19-04-2002 1 14-02-1994 1 16-09-1990 1 18-08-1990 1 15-02-1990 1 18-10-2012 1 18-06-2011 1 05-01-2012 1 23-08-2003 1 22-12-1995 1 09-12-1992 1 14-03-1995 1 21-04-1997 1 09-04-2003 1 15-03-2007 1 17-12-1995 1 03-12-2012 1 12-08-1999 1 15-08-1990 1 08-04-1996 1 18-12-1996 1 17-09-1994 1 04-06-2012 1 30-01-1992 1 18-02-1997 1 12-12-2011 1 23-08-1994 1 17-07-2005 1 10-03-1997 1 31-05-2004 1 25-02-2011 1 09-07-2001 1 01-08-2003 1 07-02-2007 1 05-04-2002 1 10-12-2005 1 18-06-1993 1 08-08-2010 1 18-11-1993 1 24-04-2013 1 28-04-2007 1 29-11-2009 1 11-06-2014 1 26-03-1990 1 20-06-2012 1 28-06-2010 1 16-07-1995 1 29-11-2004 1 29-09-2005 1 21-04-2010 1 28-03-1990 1 09-02-1993 1 04-04-2003 1 14-11-2014 1 08-02-2009 1 29-10-1991 1 01-02-1998 1 22-06-2009 1 09-05-2009 1 14-01-2005 1 21-04-2014 1 15-07-2000 1 30-09-1991 1 02-01-2004 1 04-12-2007 1 10-12-1993 1 11-05-1997 1 14-10-1992 1 02-10-2003 1 11-01-2010 1 25-01-2006 1 25-06-2002 1 20-11-2002 1 21-08-1991 1 03-06-2014 1 18-12-2013 1 12-10-1998 1 12-07-2013 1 29-06-2006 1 17-06-2005 1 05-10-1999 1 09-04-2001 1 13-10-1990 1 13-04-2006 1 02-06-2012 1 06-09-1995 1 11-09-1997 1 26-08-2004 1 08-12-2001 1 24-10-2004 1 24-09-1994 1 25-11-1994 1 01-10-2010 1 17-02-2003 1 16-12-2007 1 22-06-1990 1 10-02-1993 1 30-07-2003 1 17-07-1995 1 17-08-1994 1 27-09-2010 1 08-05-2001 1 23-05-1999 1 17-02-1995 1 13-09-2002 1 30-06-2004 1 04-04-1992 1 16-11-2004 1 18-07-2002 1 09-03-2007 1 08-01-1990 1 02-09-1996 1 06-05-2002 1 12-03-1994 1 11-12-1994 1 10-10-1993 1 10-11-1992 1 20-01-2014 1 16-09-1997 1 13-11-2014 1 12-04-2013 1 05-08-1993 1 06-08-1999 1 14-05-2006 1 28-10-1999 1 11-02-2003 1 17-09-2014 1 02-12-2011 1 25-02-2008 1 08-08-1994 1 25-05-2002 1 25-04-1991 1 06-05-1991 1 04-01-1996 1 18-02-2000 1 21-12-2006 1 22-05-1999 1 14-06-2004 1 18-10-1996 1 04-12-2005 1 27-07-2005 1 23-12-2013 1 04-05-1995 1 14-11-1999 1 29-05-2003 1 13-04-2002 1 02-10-1990 1 05-08-2012 1 28-04-2005 1 20-07-2008 1 15-03-1992 1 03-11-1991 1 18-03-2008 1 24-09-1992 1 01-12-2008 1 09-08-2002 1 09-02-2000 1 18-02-1995 1 07-06-1994 1 16-06-1993 1 05-07-2009 1 10-10-2005 1 15-06-2004 1 25-12-2001 1 05-08-2014 1 07-04-1994 1 30-12-2011 1 18-06-1998 1 10-09-1998 1 25-07-2011 1 03-10-2000 1 18-02-1999 1 25-08-2011 1 26-06-2001 1 16-03-2008 1 14-01-2008 1 03-02-1990 1 21-07-2008 1 10-04-1992 1 07-09-2006 1 09-08-2003 1 21-10-2009 1 23-07-2009 1 04-01-2009 1 18-08-1996 1 03-09-2013 1 04-02-1992 1 19-12-1998 1 08-08-2013 1 05-12-2013 1 14-01-1998 1 01-02-2011 1 07-11-2008 1 29-05-1995 1 15-11-2004 1 09-10-2012 1 26-01-1996 1 20-10-1993 1 25-06-2011 1 24-03-2007 1 22-11-2012 1 16-11-2000 1 17-08-1991 1 06-10-2012 1 22-03-1992 1 12-04-2002 1 07-04-2005 1 02-06-2002 1 21-05-2000 1 11-07-2007 1 17-02-2009 1 30-04-2007 1 19-12-1997 1 17-03-1990 1 11-11-1994 1 07-09-1999 1 16-02-2001 1 28-10-2008 1 30-03-1995 1 15-05-2000 1 23-01-1996 1 23-02-1990 1 20-08-1990 1 06-06-1993 1 18-02-2007 1 11-11-1996 1 03-07-1992 1 10-07-2012 1 03-03-2007 1 15-06-2014 1 27-06-2005 1 05-12-2009 1 08-01-2013 1 27-05-2003 1 21-02-1995 1 11-04-2005 1 23-01-2002 1 18-01-2003 1 29-01-1993 1 07-03-2008 1 26-01-1994 1 14-03-1990 1 19-04-2009 1 01-07-2005 1 08-07-1996 1 16-06-2003 1 20-09-1993 1 28-05-2014 1 19-04-2006 1 30-11-1996 1 25-05-2011 1 15-10-1996 1 21-03-1998 1 10-01-2004 1 09-01-1999 1 28-10-1997 1 28-03-2001 1 13-07-2013 1 09-12-2001 1 05-08-1997 1 03-11-2009 1 21-11-1991 1 06-03-2004 1 21-01-2002 1 02-08-1990 1 14-02-1996 1 28-01-2003 1 29-04-1990 1 12-10-2006 1 28-03-2003 1 11-03-2007 1 02-10-1992 1 04-11-1991 1 29-03-1995 1 27-06-2006 1 17-08-2011 1 22-01-2011 1 04-01-2003 1 24-05-2006 1 30-10-1997 1 14-10-2005 1 01-05-1999 1 19-06-1996 1 15-08-2000 1 07-04-1992 1 26-05-2002 1 05-05-1993 1 27-05-1994 1 22-11-2008 1 24-12-2006 1 05-08-2006 1 27-05-2002 1 30-04-2006 1 18-03-2003 1 27-11-1990 1 24-04-2012 1 21-12-1999 1 06-06-1992 1 06-05-2014 1 03-03-1992 1 12-09-2001 1 06-06-2011 1 15-07-2004 1 15-01-1992 1 21-12-1996 1 03-08-2010 1 07-01-2005 1 02-05-2007 1 31-08-2014 1 04-02-2004 1 11-06-2008 1 23-11-1997 1 19-08-1995 1 06-12-1993 1 08-02-1996 1 05-10-1991 1 26-09-1991 1 13-05-2001 1 10-05-2009 1 27-10-2001 1 08-05-1995 1 06-03-2003 1 01-07-2013 1 03-08-2009 1 27-07-1997 1 29-05-1992 1 07-12-1998 1 01-03-2010 1 26-02-2005 1 21-04-2006 1 26-08-2000 1 07-04-1998 1 31-10-1993 1 19-04-2007 1 12-10-1995 1 19-09-2004 1 20-12-1990 1 11-02-1991 1 13-11-1998 1 07-11-1994 1 19-06-2008 1 25-10-2012 1 04-03-2001 1 29-07-2012 1 17-01-2015 1 19-09-2012 1 14-07-2000 1 21-06-1994 1 13-03-2008 1 11-12-2009 1 23-10-2007 1 07-04-2014 1 22-02-1992 1 26-10-1996 1 12-12-1998 1 04-07-2007 1 19-05-1992 1 16-01-1996 1 27-01-2007 1 19-10-1992 1 24-08-2000 1 18-10-2000 1 01-06-2006 1 19-12-2004 1 27-11-2005 1 27-09-2011 1 11-07-2002 1 21-11-2006 1 02-04-1991 1 28-08-1995 1 04-07-2013 1 18-02-1990 1 08-04-2003 1 02-01-2001 1 12-12-1999 1 17-06-2006 1 05-01-2013 1 18-01-2014 1 17-12-2003 1 10-04-2013 1 05-03-2009 1 12-12-1991 1 31-03-2005 1 25-08-2012 1 22-10-2011 1 09-01-2002 1 14-11-2010 1 30-01-2005 1 28-02-2004 1 30-08-2000 1 05-08-1996 1 31-07-2011 1 09-12-2006 1 28-08-2005 1 22-02-1994 1 28-01-1998 1 01-11-1991 1 20-11-2005 1 08-03-1998 1 20-03-1992 1 15-08-2011 1 27-01-1991 1 20-08-1996 1 07-12-1992 1 14-01-1999 1 20-11-1997 1 28-10-2006 1 17-11-2004 1 07-10-1990 1 28-12-1998 1 05-07-2001 1 05-02-1994 1 04-03-2000 1 30-07-1992 1 13-02-1991 1 06-03-2010 1 03-06-2009 1 30-11-1993 1 08-07-1990 1 25-04-2002 1 01-09-1992 1 15-04-2004 1 11-03-2014 1 07-04-2010 1 16-05-2001 1 20-09-1999 1 24-01-2010 1 22-07-2004 1 25-11-2006 1 12-02-1998 1 21-08-1994 1 02-09-2011 1 01-05-2010 1 19-09-2009 1 19-10-2001 1 31-12-2003 1 11-02-1994 1 31-10-2013 1 02-02-1996 1 06-02-2009 1 16-11-2007 1 07-01-2011 1 27-08-2014 1 19-10-1999 1 07-07-1997 1 08-09-2009 1 10-09-2002 1 23-06-2012 1 20-07-2007 1 25-11-2002 1 08-03-2009 1 05-07-1999 1 17-05-1994 1 27-12-2000 1 15-02-1993 1 16-09-2009 1 26-10-2012 1 16-03-1998 1 24-06-2014 1 02-10-1997 1 24-09-2001 1 23-07-1992 1 30-06-2002 1 31-07-1999 1 07-12-2001 1 04-01-2002 1 28-11-1991 1 11-07-1996 1 17-06-2008 1 21-12-2013 1 10-09-2000 1 16-12-2011 1 03-10-2014 1 10-04-2009 1 28-07-2002 1 21-03-1997 1 06-09-2008 1 29-06-2002 1 07-08-1991 1 19-01-1996 1 24-04-2007 1 28-09-2011 1 12-10-1997 1 25-01-2008 1 08-06-2005 1 20-04-2004 1 27-02-2010 1 29-11-1999 1 23-08-2005 1 14-05-2001 1 03-04-2001 1 22-01-1993 1 05-01-2014 1 06-12-1991 1 29-04-2010 1 06-11-2013 1 29-02-1992 1 06-12-1995 1 09-08-1990 1 28-10-1998 1 07-11-1992 1 02-12-2012 1 13-04-2003 1 07-11-2012 1 13-12-2014 1 21-10-2005 1 19-06-1992 1 27-06-2008 1 11-10-2012 1 11-12-1998 1 02-11-2010 1 20-08-1994 1 03-11-1990 1 06-06-2014 1 01-05-1997 1 02-01-1991 1 01-01-2008 1 19-10-1990 1 11-08-1999 1 22-07-1999 1 30-08-2012 1 02-06-2010 1 21-02-1999 1 18-08-2006 1 18-01-1997 1 04-12-2013 1 24-05-2003 1 06-01-2011 1 05-01-1991 1 10-03-1991 1 16-08-2003 1 29-10-2006 1 04-08-1997 1 10-02-1997 1 26-08-2013 1 29-06-2009 1 15-08-1999 1 10-05-2012 1 27-06-1996 1 01-04-2002 1 14-11-2005 1 03-08-1993 1 23-03-2009 1 25-07-1995 1 16-03-2014 1 31-01-2011 1 16-06-1991 1 14-04-1993 1 15-07-2011 1 29-07-1995 1 16-09-2013 1 21-01-2009 1 25-04-2007 1 11-10-1994 1 20-04-2013 1 12-05-2007 1 02-02-2001 1 13-10-1991 1 03-09-1991 1 25-12-1997 1 14-02-1992 1 10-03-2004 1 29-09-2001 1 13-11-1995 1 22-04-2010 1 21-08-2010 1 24-10-1997 1 09-11-1991 1 22-02-2015 1 16-04-1995 1 18-06-2000 1 01-10-2001 1 24-07-2012 1 02-08-1992 1 17-03-2010 1 06-12-1996 1 10-08-2006 1 03-02-1994 1 03-02-1993 1 15-08-2002 1 05-12-2007 1 09-02-2007 1 08-02-1990 1 01-10-2006 1 16-03-2003 1 30-03-1999 1 14-02-1990 1 01-08-2010 1 20-01-1996 1 29-12-2010 1 30-04-2013 1 23-07-2014 1 09-11-1992 1 25-03-2007 1 16-06-2006 1 11-03-1996 1 29-10-1993 1 02-11-2012 1 03-06-1991 1 08-06-2002 1 18-06-2010 1 24-07-2007 1 18-06-1997 1 30-09-1993 1 28-03-1995 1 02-08-2006 1 28-08-1998 1 20-11-1991 1 25-12-1991 1 23-08-2009 1 24-07-1999 1 18-08-2007 1 18-02-2011 1 02-04-2002 1 05-05-1991 1 02-08-1991 1 10-04-1994 1 26-09-2010 1 29-02-2004 1 31-12-2012 1 25-02-2005 1 07-03-2005 1 01-11-2014 1 06-08-1994 1 12-08-2004 1 17-09-2003 1 27-12-2001 1 16-01-2013 1 24-12-2012 1 27-04-2012 1 29-11-2001 1 04-03-2014 1 21-08-2006 1 18-03-1990 1 03-04-2013 1 06-12-2006 1 23-07-1996 1 28-09-2007 1 20-11-2010 1 17-09-2011 1 20-11-2008 1 01-03-1991 1 13-08-2004 1 24-06-1998 1 30-08-1997 1 04-02-2011 1 17-04-2005 1 24-03-2006 1 28-03-2006 1 25-11-1991 1 13-01-1991 1 27-01-2000 1 08-10-1995 1 09-05-2008 1 20-02-2001 1 24-02-2012 1 13-12-1993 1 23-01-2003 1 22-07-2002 1 03-02-2008 1 04-06-1996 1 21-09-1990 1 13-12-1995 1 28-11-2002 1 18-05-1993 1 14-03-2012 1 28-01-1993 1 10-02-2002 1 05-11-2000 1 27-11-1992 1 18-10-2006 1 01-12-2000 1 03-02-2006 1 24-10-2007 1 19-09-2007 1 28-02-2010 1 11-10-1992 1 16-11-1991 1 04-03-2006 1 16-07-2014 1 28-02-1997 1 28-10-2007 1 17-07-2012 1 16-07-1991 1 03-08-1994 1 29-07-1996 1 28-09-1994 1 04-11-2004 1 28-07-2014 1 05-08-2005 1 29-07-2009 1 21-04-2001 1 10-02-2008 1 22-09-2002 1 04-11-2010 1 14-11-2007 1 01-06-1997 1 23-02-2014 1 24-05-2004 1 25-11-2004 1 10-02-1994 1 04-03-2003 1 15-02-2011 1 01-05-2013 1 03-01-2015 1 23-01-2013 1 11-09-2004 1 25-08-1990 1 26-06-2009 1 13-04-1991 1 28-12-1999 1 11-07-2001 1 09-11-2008 1 01-04-1994 1 21-04-2008 1 22-12-2012 1 20-05-1990 1 11-09-2010 1 17-04-1999 1 07-07-2013 1 06-02-2006 1 24-01-2009 1 30-06-2000 1 21-06-1997 1 29-03-2005 1 04-02-2013 1 16-05-1990 1 27-01-1990 1 28-12-2004 1 29-09-1997 1 07-05-1990 1 18-09-1999 1 08-05-2005 1 11-05-2010 1 02-08-2009 1 15-10-2005 1 26-01-2011 1 10-04-1996 1 23-04-1995 1 18-10-1995 1 16-12-1998 1 18-07-1991 1 14-09-2001 1 09-03-1999 1 24-06-2003 1 26-08-2012 1 01-03-1997 1 19-05-1993 1 06-07-1996 1 09-12-2005 1 22-09-1990 1 29-08-1999 1 06-11-2001 1 07-08-1994 1 11-02-2010 1 12-07-2006 1 06-10-2002 1 19-12-2011 1 10-06-2001 1 15-02-2001 1 23-08-1996 1 07-08-1992 1 05-07-2003 1 10-02-2007 1 28-12-2014 1 17-10-2014 1 21-07-1996 1 02-07-1991 1 07-01-2008 1 11-10-2003 1 13-02-2003 1 26-06-2000 1 16-07-2004 1 04-03-2007 1 09-10-2009 1 09-07-2013 1 14-03-2007 1 07-12-1993 1 12-02-2009 1 23-02-1993 1 28-01-1992 1 16-07-2003 1 24-06-2009 1 04-07-1997 1 18-04-1993 1 21-03-2008 1 03-02-1999 1 06-09-2000 1 11-11-2013 1 09-10-1995 1 25-10-2007 1 09-10-1990 1 20-03-1991 1 19-12-2001 1 08-11-2009 2 27-07-2014 2 25-12-2013 2 11-03-2010 2 15-05-1997 2 16-05-2008 2 28-12-2002 2 16-07-2002 2 20-07-1991 2 07-12-1995 2 04-06-2000 2 03-02-1997 2 28-01-2010 2 21-09-1996 2 09-08-2004 2 14-12-1991 2 04-05-2000 2 05-01-1992 2 19-09-1995 2 03-01-2004 2 07-04-1999 2 30-08-1993 2 28-12-1991 2 09-07-2002 2 29-09-1999 2 07-12-1999 2 21-09-2005 2 25-09-2001 2 05-07-2014 2 11-11-1998 2 15-11-1997 2 09-03-2003 2 20-09-1990 2 25-05-1990 2 07-07-1996 2 24-06-1990 2 21-12-2002 2 06-05-2007 2 14-07-1997 2 07-11-1997 2 22-08-1991 2 14-04-1992 2 29-01-1998 2 05-08-1992 3 28-04-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 07-02-2015 10 25-01-2015 10 11-02-2015 10 10-02-2015 10 19-02-2015 10 29-01-2015 11 26-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 27-01-2015 13 23-01-2015 13 03-02-2015 13 09-02-2015 13 20-02-2015 14 27-02-2015 14 22-01-2015 14 15-01-2015 15 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-02-2015 16 16-01-2015 16 16-02-2015 16 05-02-2015 16 13-02-2015 16 06-01-2015 17 09-01-2015 17 24-02-2015 17 08-02-2015 17 26-02-2015 17 20-01-2015 18 28-02-2015 18 14-02-2015 18 18-01-2015 18 03-01-2015 18 25-02-2015 18 01-02-2015 18 14-01-2015 19 01-01-2015 19 21-01-2015 19 12-01-2015 19 23-02-2015 19 21-02-2015 19 31-01-2015 20 12-02-2015 20 22-02-2015 20 06-02-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 10-01-2015 24 04-02-2015 24 24-01-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_SEVERITY : 4 Trivial Damage 90 Major Damage 276 Total Loss 280 Minor Damage 354 Name: incident_severity, dtype: int64 INCIDENT_STATE : 7 OH 23 PA 30 NC 110 VA 110 WV 217 SC 248 NY 262 Name: incident_state, dtype: int64 INCIDENT_LOCATION : 1000 3340 3rd Hwy 1 3457 Texas Lane 1 9103 MLK Lane 1 1507 Solo Ave 1 4653 Pine St 1 6684 Solo Lane 1 9580 MLK Ave 1 3006 Lincoln Ridge 1 1358 Maple St 1 3029 5th Ave 1 2217 Tree Lane 1 4335 1st St 1 5189 Francis Drive 1 1553 Lincoln St 1 3660 Andromedia Hwy 1 5812 Weaver Ave 1 5499 Elm Hwy 1 4567 Pine Ave 1 7511 1st Ave 1 4710 Lincoln Hwy 1 5778 Pine Ridge 1 4143 Maple Ridge 1 4175 Elm Ridge 1 8281 Lincoln Lane 1 1725 Solo Lane 1 3929 Elm Ave 1 9787 Andromedia Ave 1 5894 Flute Drive 1 2100 Francis Drive 1 1298 Maple Hwy 1 2774 Apache Drive 1 8617 Best Ave 1 9720 Lincoln Hwy 1 3495 Britain Drive 1 6359 MLK Ridge 1 1331 Elm Ridge 1 6655 5th Drive 1 9988 Rock Ridge 1 6582 Elm Lane 1 2290 4th Ave 1 1529 Elm Ridge 1 5678 Lincoln Drive 1 9397 5th Hwy 1 1562 Britain St 1 9439 MLK St 1 7240 5th Ridge 1 5790 Flute Ridge 1 2873 Flute Ave 1 3592 MLK Ridge 1 3221 Solo Ridge 1 4239 Weaver Ave 1 7523 Oak Lane 1 2889 Francis St 1 9523 Solo Hwy 1 6375 2nd Lane 1 7314 Tree Drive 1 9278 Francis Ridge 1 5201 Texas Hwy 1 7601 Andromedia Lane 1 2644 Elm Drive 1 4972 Francis Lane 1 2123 MLK Ridge 1 2204 Washington Lane 1 9239 Washington Ridge 1 7846 Andromedia Drive 1 9760 Solo Lane 1 2577 Texas Ridge 1 8876 1st St 1 5028 Maple Ridge 1 9105 Tree Lane 1 2654 Embaracadero St 1 6834 1st Drive 1 3553 Texas Ave 1 6399 Oak Drive 1 6179 3rd Ridge 1 5821 2nd St 1 7628 4th Lane 1 7488 Lincoln Lane 1 4857 Weaver St 1 2920 5th Ave 1 8118 Elm Ridge 1 8576 Andromedia St 1 5104 Francis Drive 1 3929 Oak Drive 1 1989 Solo Lane 1 8864 Tree Ridge 1 6250 1st Ridge 1 2654 Elm Drive 1 1655 Francis Hwy 1 3926 Rock Lane 1 4642 Rock Ridge 1 9214 Texas Drive 1 6149 Best Ridge 1 4814 Lincoln Lane 1 5071 Flute Ridge 1 1833 Solo Ave 1 4232 Britain Ridge 1 5783 Oak Ave 1 4939 Best St 1 6956 Maple Drive 1 2968 Andromedia Ave 1 1364 Best St 1 6618 Cherokee Drive 1 6092 5th Ave 1 5474 Weaver Hwy 1 1580 Maple Lane 1 8269 Sky Hwy 1 2324 Texas Ridge 1 4546 Tree St 1 4964 Elm Lane 1 5994 5th Ave 1 8014 Embaracadero Drive 1 4154 Lincoln Hwy 1 3966 Francis Ridge 1 5838 Pine Lane 1 6678 Weaver Drive 1 3884 Pine Lane 1 8211 Sky Hwy 1 9418 5th Hwy 1 5071 1st Lane 1 6378 Britain Ave 1 5971 5th Hwy 1 1173 Andromedia Ave 1 6158 Sky Ridge 1 4633 5th Lane 1 1437 3rd Lane 1 7900 Sky Hwy 1 4699 Texas Ridge 1 8080 Oak Lane 1 9214 Elm Ridge 1 8214 Flute St 1 1679 2nd Hwy 1 2272 Embaracadero Drive 1 1328 Texas Lane 1 2275 Best Lane 1 8100 3rd Ave 1 3567 4th Drive 1 3998 Flute St 1 3540 Maple St 1 6309 Cherokee Ave 1 3311 2nd Drive 1 8336 1st Ridge 1 3796 Cherokee Drive 1 6550 Andromedia St 1 6751 5th Hwy 1 8983 Tree St 1 3416 Washington Drive 1 9038 2nd Lane 1 3087 Oak Hwy 1 7756 Solo Drive 1 1824 5th Lane 1 1316 Britain Ridge 1 7583 Washington Ave 1 7098 Lincoln Hwy 1 1267 Francis Hwy 1 7825 1st Ridge 1 7721 Washington Ridge 1 5279 Pine Ridge 1 8492 Andromedia Ridge 1 6957 Weaver Drive 1 3618 Sky Ave 1 6408 Weaver Ridge 1 7630 Rock Drive 1 7253 MLK St 1 4866 4th Hwy 1 3770 Flute Drive 1 6981 Weaver St 1 4492 Andromedia Ave 1 6440 Rock Lane 1 1353 Washington St 1 4486 Cherokee Ridge 1 7575 Pine St 1 3220 Rock Drive 1 2725 Britain Ridge 1 6409 Cherokee Drive 1 7649 Texas St 1 9818 Cherokee Ave 1 8538 Texas Lane 1 6206 3rd Ridge 1 3184 Oak Ave 1 9685 Sky Ridge 1 5771 Best St 1 7434 Oak Hwy 1 7644 Tree Ridge 1 2526 Embaracadero Ave 1 7061 Cherokee Drive 1 1102 Apache Hwy 1 2862 Tree Ridge 1 6723 Best Drive 1 1248 MLK Ridge 1 3982 Washington Hwy 1 5806 Embaracadero St 1 9067 Texas Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 6619 Flute Ave 1 7295 Tree Hwy 1 3320 5th Hwy 1 2603 Andromedia Hwy 1 6874 Maple Ridge 1 4862 Lincoln Hwy 1 3419 Apache St 1 5608 Solo St 1 9240 Britain Ave 1 1416 Cherokee Ridge 1 4672 MLK St 1 9821 Francis Ave 1 2193 4th Ridge 1 8689 Maple Hwy 1 7859 4th Ridge 1 7928 Maple Ridge 1 5211 Weaver Drive 1 4907 Andromedia Drive 1 5639 1st Ridge 1 9706 MLK Lane 1 4538 Flute Hwy 1 9633 MLK Lane 1 7709 Rock Lane 1 6939 3rd Hwy 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 2457 Washington Ave 1 7773 Tree Hwy 1 9373 Pine Hwy 1 3492 Flute Lane 1 7082 Oak Ridge 1 8822 Sky St 1 9911 Britain Lane 1 9728 Britain Hwy 1 6058 Andromedia Hwy 1 4460 4th Lane 1 5719 2nd Lane 1 9177 Texas Ave 1 9730 2nd Hwy 1 6985 Maple Lane 1 6467 Best Ave 1 6838 Flute Lane 1 1810 Elm Hwy 1 2500 Tree St 1 6971 Best Ridge 1 6859 Flute Ridge 1 8404 Embaracadero St 1 9633 4th St 1 2469 Francis Lane 1 2492 Lincoln Lane 1 1830 Sky St 1 2220 1st Lane 1 6317 Best St 1 6848 Elm Hwy 1 8917 Tree Ridge 1 2950 MLK Ave 1 7576 Pine Ridge 1 9744 Texas Drive 1 1110 4th Drive 1 5312 Francis Ridge 1 6530 Weaver Ave 1 4955 Lincoln Ridge 1 1365 Francis Ave 1 4237 4th St 1 6604 Apache Drive 1 7797 Tree Ridge 1 8758 5th St 1 8907 Tree Ave 1 2798 1st Ave 1 9417 Tree Hwy 1 2333 Maple Lane 1 4618 Flute Ave 1 8906 Elm Lane 1 4755 Best Lane 1 5846 Weaver Drive 1 2005 Texas Hwy 1 6259 Weaver St 1 1491 Francis Ridge 1 2381 1st Hwy 1 4614 MLK Ave 1 9360 3rd Drive 1 2037 5th Drive 1 2352 Sky Drive 1 7877 3rd Ridge 1 8742 4th St 1 6738 Washington Hwy 1 5780 4th Ave 1 3039 Oak Hwy 1 7897 Lincoln St 1 8456 1st Ave 1 6706 Francis Drive 1 1320 Flute Lane 1 1840 Embaracadero Ave 1 9138 1st St 1 8212 Flute Ridge 1 8548 Cherokee Ridge 1 7791 Britain Ridge 1 9935 4th Drive 1 6355 4th Hwy 1 9588 Solo St 1 6068 2nd St 1 3167 4th Ridge 1 5333 MLK Lane 1 3028 5th St 1 8493 Apache Drive 1 9070 Tree Ave 1 1956 Apache St 1 8198 Embaracadero Lane 1 1331 Britain Hwy 1 8782 3rd St 1 9737 Solo Hwy 1 5418 Britain Ave 1 5663 Oak Lane 1 3841 Washington Lane 1 6934 Lincoln Ave 1 2533 Elm St 1 3915 Embaracadero St 1 3834 Pine St 1 5475 Rock Lane 1 6889 Cherokee St 1 7466 MLK Ridge 1 4872 Rock Ridge 1 8834 Elm Drive 1 5341 5th Ave 1 5904 1st Drive 1 2376 Sky Ridge 1 7108 Tree St 1 7380 5th Hwy 1 3936 Tree Drive 1 6451 1st Hwy 1 5650 Sky Drive 1 6668 Andromedia Ridge 1 4625 MLK Drive 1 1469 Lincoln Drive 1 3998 4th Hwy 1 1951 Best Ave 1 8368 Cherokee Ave 1 8624 Francis Ave 1 4394 Oak St 1 8991 Texas Hwy 1 7930 Texas Ave 1 2617 Andromedia Drive 1 8006 Maple Hwy 1 9639 Britain Ridge 1 2820 Britain St 1 4519 Embaracadero St 1 7327 Lincoln Drive 1 2123 Texas Ave 1 8453 Elm St 1 3753 Francis Lane 1 6137 MLK St 1 8949 Rock Hwy 1 4475 Lincoln Ridge 1 1546 Cherokee Ave 1 7571 Elm Ridge 1 5168 5th Ave 1 7973 4th St 1 4119 Texas St 1 3797 Solo Lane 1 4885 Oak Lane 1 1919 4th Lane 1 9082 3rd Lane 1 4434 Weaver St 1 1515 Embaracadero St 1 7042 Maple Ridge 1 3952 Andromedia Lane 1 3925 Sky St 1 2299 1st St 1 6751 Pine Ridge 1 8203 Lincoln Ave 1 3697 Apache Drive 1 3818 Texas Ridge 1 1123 5th Lane 1 4242 Rock Lane 1 4702 Texas Drive 1 3656 Solo Ave 1 9286 Oak Ave 1 8701 5th Lane 1 4981 Weaver St 1 8306 1st Drive 1 7745 Washington Ridge 1 1386 Britain St 1 5969 Francis St 1 7168 Andromedia Ridge 1 1472 4th Drive 1 6676 Tree Lane 1 8941 Solo Ridge 1 1186 Rock St 1 2537 5th Ave 1 2804 Best St 1 6581 Rock Ridge 1 1275 4th Ridge 1 9942 Tree Ave 1 3196 Cherokee St 1 2318 Washington Hwy 1 3376 5th Drive 1 6851 3rd Drive 1 2865 Maple Lane 1 4347 2nd Ridge 1 9682 Cherokee Ridge 1 1213 4th Lane 1 9352 Washington Ave 1 6256 Elm St 1 5226 Maple St 1 1910 Sky Ave 1 1815 Cherokee Drive 1 4574 Britain Hwy 1 2280 4th Ave 1 6888 Elm Ridge 1 7397 4th Drive 1 2757 4th Hwy 1 8078 Britain Hwy 1 3127 Flute St 1 1346 5th Lane 1 1532 Washington St 1 6501 5th Drive 1 8940 Elm Ave 1 5649 Texas Ave 1 9719 4th Lane 1 1273 Rock Lane 1 3842 Solo Ridge 1 8766 Lincoln Lane 1 7236 Apache Lane 1 7544 Washington Ave 1 8096 Apache Hwy 1 3525 3rd Hwy 1 4826 5th St 1 9224 Sky Drive 1 4020 Best Drive 1 3809 Texas Lane 1 7495 Washington Ave 1 9020 Elm Ave 1 2808 Elm St 1 6492 4th Lane 1 2003 Maple Hwy 1 3618 Maple Lane 1 9657 5th Ave 1 8805 Cherokee Drive 1 3061 Francis Hwy 1 8621 Best Ridge 1 6605 Tree Ave 1 9573 Weaver Ave 1 8101 3rd Ridge 1 7460 Apache Lane 1 5318 5th Ave 1 8638 3rd Ave 1 7693 Cherokee Lane 1 3066 Francis Ave 1 4835 Britain Ridge 1 4629 Elm Ridge 1 1705 Weaver St 1 6309 5th Ave 1 2397 Cherokee Ave 1 6658 Weaver St 1 9742 5th Ridge 1 9034 Weaver Ridge 1 8381 Solo Hwy 1 9316 Pine Ave 1 5630 1st Drive 1 5997 Embaracadero Drive 1 8832 Pine Drive 1 6677 Andromedia Drive 1 4994 Lincoln Drive 1 9847 Elm St 1 5602 Britain St 1 1809 Sky St 1 1555 Washington Lane 1 1916 Elm St 1 7782 Rock St 1 5532 Francis Lane 1 6741 Oak Ridge 1 7693 Britain Lane 1 1128 Maple Lane 1 5280 Pine Ave 1 5532 Weaver Ridge 1 9816 Britain St 1 8204 Pine Lane 1 4627 Elm Ridge 1 3092 Texas Drive 1 1914 Francis St 1 5925 Tree Hwy 1 3397 5th Ave 1 7281 Oak St 1 4447 Francis Hwy 1 1845 Best St 1 1325 1st Lane 1 2697 Oak Drive 1 9724 Maple St 1 7121 Britain Drive 1 8542 Lincoln Ridge 1 5061 Francis Ave 1 2777 Solo Drive 1 6493 Lincoln Lane 1 1953 Sky Lane 1 1618 Maple Hwy 1 1371 Texas Lane 1 6193 1st Hwy 1 3653 Elm Drive 1 4429 Washington St 1 3707 Oak Ridge 1 3664 Francis Ridge 1 8667 Weaver Lane 1 7477 MLK Drive 1 2903 Weaver Drive 1 7582 Pine Drive 1 4369 Maple Lane 1 5191 4th St 1 4585 Francis Ave 1 4176 Britain Hwy 1 3847 Elm Hwy 1 2832 Andromedia Lane 1 6931 Elm St 1 4755 1st St 1 2199 Texas Drive 1 7976 Britain Drive 1 8489 Pine Hwy 1 8983 Francis Ridge 1 4876 Washington Drive 1 7705 Lincoln Drive 1 6516 Solo Drive 1 4214 MLK Ridge 1 4231 3rd Ave 1 1738 Solo Lane 1 5743 4th Ridge 1 8212 Rock Ave 1 2823 Weaver Lane 1 3052 Weaver Ridge 1 7783 Lincoln Hwy 1 6515 Oak Lane 1 5779 2nd Lane 1 8021 Flute Ave 1 3414 Elm Ave 1 3995 Lincoln Hwy 1 7733 Britain Lane 1 9322 Rock Hwy 1 9325 Lincoln Drive 1 1558 1st Ridge 1 3275 Pine St 1 6428 Andromedia Lane 1 2711 Britain Ave 1 9573 2nd Ave 1 3439 Andromedia Hwy 1 7909 Andromedia Hwy 1 7684 Francis Ridge 1 8509 Apache St 1 4554 Sky Ave 1 6435 Texas Ave 1 8845 5th Ave 1 3288 Tree Lane 1 8926 Texas Ridge 1 3799 Embaracadero Drive 1 2756 Britain Hwy 1 5874 1st Hwy 1 7331 Sky Hwy 1 8821 Elm St 1 9422 Washington Ridge 1 2986 MLK Drive 1 9633 Rock Hwy 1 2651 MLK Lane 1 3263 Pine Ridge 1 6583 MLK Ridge 1 6608 Apache Lane 1 1699 Oak Drive 1 6315 2nd Lane 1 1012 5th Lane 1 8954 Apache Lane 1 3102 Apache St 1 3903 Oak Ave 1 4188 Britain Ave 1 3246 Britain Ridge 1 7780 Flute Lane 1 4577 Sky Hwy 1 2430 MLK Ave 1 7281 Maple Hwy 1 8655 Cherokee Lane 1 5584 Britain Lane 1 8964 Francis St 1 8188 Tree Ave 1 6303 1st Drive 1 3693 Pine Ave 1 9878 Washington Ave 1 6011 Britain St 1 6456 Andromedia Drive 1 2311 4th St 1 8215 Flute Drive 1 8639 5th Hwy 1 3751 Tree Hwy 1 6724 Andromedia St 1 7756 Pine Hwy 1 3486 Flute Ave 1 4780 Best Drive 1 5431 3rd Ridge 1 7740 MLK St 1 5459 MLK Ave 1 1240 Tree Lane 1 9489 3rd St 1 7002 Oak Hwy 1 1628 Best Drive 1 6045 Andromedia St 1 1220 MLK Ave 1 7316 Texas Ave 1 5985 Lincoln Lane 1 6128 Elm Lane 1 9153 3rd Hwy 1 8862 Maple Ridge 1 8081 Flute Ridge 1 4098 Weaver Ridge 1 3089 Oak Ridge 1 5771 Sky Ave 1 6603 Francis Hwy 1 8920 Best Ave 1 8492 Weaver Hwy 1 3327 Lincoln Drive 1 4264 Lincoln Ridge 1 1992 Britain Drive 1 1762 Maple Hwy 1 4279 Solo Drive 1 4431 Rock St 1 5839 Weaver Lane 1 2900 Sky Drive 1 2914 Oak Drive 1 1589 Pine St 1 1880 Weaver Drive 1 3900 Texas St 1 4793 4th Ridge 1 9154 MLK Hwy 1 3104 Sky Drive 1 4558 3rd Hwy 1 5051 Elm St 1 1536 Flute Drive 1 5058 4th Lane 1 6681 Texas Ridge 1 4693 Lincoln Hwy 1 6494 4th Ave 1 1269 Flute Drive 1 7574 4th St 1 8879 1st Drive 1 3097 4th Drive 1 6110 Rock Ridge 1 4985 Sky Lane 1 4477 5th Ave 1 2293 Washington Ave 1 4782 Sky Lane 1 7717 Britain Hwy 1 4937 Flute Drive 1 1941 5th Ridge 1 8085 Andromedia St 1 5914 Oak Ave 1 5074 3rd St 1 5383 Maple Drive 1 4053 Sky Lane 1 6853 Sky Hwy 1 5007 Oak St 1 9875 MLK Ave 1 4910 1st Lane 1 7609 Rock St 1 1215 Pine Hwy 1 7299 Apache St 1 6574 4th Drive 1 7877 Sky Lane 1 2078 3rd Ave 1 4390 4th Drive 1 9751 Sky Ridge 1 5765 Washington St 1 5480 3rd Ridge 1 6738 Francis Hwy 1 1388 Embaracadero Hwy 1 3492 Britain St 1 6955 Pine Drive 1 5178 Weaver Hwy 1 5812 3rd Hwy 1 9138 3rd St 1 9317 Apache Ave 1 3878 Tree Lane 1 9748 Sky Drive 1 2644 MLK Drive 1 4905 Best Lane 1 9734 2nd Ridge 1 5622 Best Ridge 1 9751 Tree St 1 7459 Flute St 1 4158 Washington Lane 1 5249 4th Ave 1 3808 5th Ave 1 6044 Weaver Drive 1 7223 Embaracadero St 1 3846 4th Hwy 1 3790 Andromedia Hwy 1 3508 Washington St 1 6719 Flute St 1 9562 4th Ridge 1 3706 4th Hwy 1 3098 Oak Lane 1 9910 Maple Ave 1 1512 Rock Lane 1 3422 Flute St 1 1643 Washington Hwy 1 5621 4th Ave 1 2509 Rock Drive 1 5586 2nd St 1 6634 Texas Ridge 1 9369 Flute Hwy 1 3423 Francis Ave 1 3172 Tree Ridge 1 1030 Pine Lane 1 7142 5th Lane 1 1832 Elm Hwy 1 5093 Flute Lane 1 3171 Andromedia Lane 1 7178 Best Drive 1 8150 Washington Ridge 1 5540 Sky St 1 5585 Washington Drive 1 7121 Rock St 1 1879 4th Lane 1 7066 Texas Ave 1 2886 Tree Ridge 1 9798 Sky Ridge 1 1589 Best Ave 1 3659 Oak Lane 1 4981 Flute Hwy 1 5260 Francis Drive 1 9358 Texas Ridge 1 3289 Britain Drive 1 7819 2nd Ave 1 6067 Weaver Ridge 1 4687 5th Drive 1 3323 1st Lane 1 5160 2nd Hwy 1 4931 Maple Drive 1 7393 Washington St 1 7502 Rock Lane 1 9397 Francis St 1 1923 2nd Hwy 1 3814 Britain Drive 1 3100 Best St 1 1929 Britain Drive 1 4095 MLK St 1 3966 Oak Hwy 1 8973 Washington St 1 5455 Oak Hwy 1 8995 1st Ave 1 5812 Oak St 1 7069 4th Hwy 1 2577 Washington Drive 1 2494 Andromedia Drive 1 8049 4th St 1 1681 Cherokee Hwy 1 4254 Best Ridge 1 5782 Rock Drive 1 7447 Lincoln Ridge 1 4983 MLK Ridge 1 7041 Tree Ridge 1 5640 Embaracadero Lane 1 5380 Pine St 1 7816 MLK Lane 1 7701 Tree St 1 1707 Sky Ave 1 2087 Apache Ave 1 7144 Andromedia St 1 6390 Apache St 1 4539 Texas St 1 5865 Sky Lane 1 4055 2nd Drive 1 2849 Pine Drive 1 2889 Weaver St 1 5277 Texas Lane 1 8668 Flute St 1 8618 Texas Lane 1 3930 Embaracadero St 1 2905 Embaracadero Drive 1 9236 2nd Hwy 1 6443 Washington Ridge 1 9603 Texas Lane 1 6717 Best Drive 1 8167 Apache Ave 1 1376 Pine St 1 6731 Andromedia Hwy 1 4721 Cherokee Hwy 1 2253 Maple Ave 1 9918 Andromedia Drive 1 5276 2nd Lane 1 9007 Francis Hwy 1 5352 Lincoln Drive 1 8245 4th Hwy 1 6660 MLK Drive 1 4545 4th Ridge 1 9101 2nd Hwy 1 8770 1st Lane 1 1957 Washington Ave 1 6608 MLK Hwy 1 7121 Francis Lane 1 6357 Texas Lane 1 4615 Embaracadero Ave 1 7552 3rd St 1 4296 Pine Hwy 1 1218 Sky Hwy 1 3769 Sky St 1 7954 Tree Ridge 1 5862 Apache Ridge 1 3771 4th St 1 1578 5th Lane 1 1985 5th Ave 1 8097 Maple Lane 1 7428 Sky Hwy 1 8602 Washington Ridge 1 8353 Britain Ridge 1 3122 Apache Drive 1 6638 Tree Drive 1 8125 Texas Ridge 1 4414 Solo Drive 1 3777 Maple Ave 1 4268 2nd Ave 1 1229 5th Ave 1 7238 2nd St 1 3227 Maple Ave 1 1306 Andromedia St 1 5901 Elm Drive 1 6702 Andromedia St 1 1381 Francis Ave 1 2299 Britain Drive 1 6945 Texas Hwy 1 8718 Apache Lane 1 3658 Rock Drive 1 6035 Rock Ave 1 7476 4th St 1 3041 3rd Ave 1 9245 Weaver Ridge 1 4107 MLK Ridge 1 6522 Apache Drive 1 6117 4th Ave 1 7923 Elm Ave 1 1741 Best Ridge 1 1806 Weaver Ridge 1 3488 Flute Lane 1 2539 Embaracadero Ridge 1 1133 Apache St 1 1654 Pine St 1 5769 Texas Lane 1 8704 Britain Lane 1 5868 Best Drive 1 6104 Oak Ave 1 2668 Cherokee St 1 9488 Best Drive 1 9856 Apache St 1 7629 5th St 1 6331 MLK Ave 1 8579 Apache Drive 1 2289 Weaver Ridge 1 6429 4th Hwy 1 9081 Cherokee Hwy 1 7535 5th Lane 1 8477 Francis Hwy 1 7201 Washington Ave 1 2230 1st St 1 9610 Cherokee St 1 3743 Andromedia Ridge 1 6479 Francis Ave 1 3418 Texas Lane 1 2086 Francis Drive 1 7197 2nd Drive 1 8459 Apache Ave 1 9043 Maple Hwy 1 9760 4th Hwy 1 4291 Sky Hwy 1 9078 Francis Ridge 1 2003 2nd Hwy 1 8586 1st Ridge 1 1091 1st Drive 1 3053 Lincoln Drive 1 9148 4th Hwy 1 2100 MLK St 1 8809 Flute St 1 1620 Oak Ave 1 7529 Solo Ridge 1 5269 Flute Hwy 1 2337 Lincoln Hwy 1 4453 Best Ave 1 5650 Rock Ave 1 5236 Weaver Drive 1 2878 Britain Hwy 1 2014 Rock Ave 1 9066 Best Ridge 1 1818 Tree St 1 5868 Sky Hwy 1 8064 4th Ave 1 3555 Francis Ridge 1 9611 Pine Ridge 1 4671 5th Ridge 1 6329 Apache Ave 1 9929 Rock Drive 1 4234 Cherokee Lane 1 2646 MLK Drive 1 9484 Pine Drive 1 7500 Texas Ridge 1 3177 MLK Ridge 1 4116 Embaracadero Lane 1 7162 Maple Ave 1 8749 Tree St 1 7570 Cherokee Drive 1 4496 Pine Lane 1 3639 Flute Hwy 1 1028 Sky Lane 1 1454 5th Ridge 1 8957 Weaver Drive 1 1087 Flute Drive 1 6484 Tree Drive 1 3447 Solo Ave 1 2306 5th Lane 1 8811 Maple Hwy 1 7615 Weaver Drive 1 7405 Oak St 1 8524 Pine Lane 1 9529 4th Drive 1 8897 Sky St 1 7504 Flute Drive 1 9293 Pine Lane 1 3550 Washington Ave 1 2753 Cherokee Ave 1 7155 Apache Drive 1 4939 Oak Lane 1 2980 Sky Ridge 1 7551 Britain Lane 1 3805 Lincoln Hwy 1 2787 MLK St 1 5924 Maple Drive 1 6165 Rock Ridge 1 3167 2nd St 1 6536 MLK Hwy 1 1540 Apache Lane 1 7835 Cherokee Hwy 1 3094 Best Lane 1 3982 Weaver Lane 1 4995 Weaver Ridge 1 9980 Lincoln Ave 1 3590 Best Hwy 1 3443 Maple Ridge 1 2809 Francis Lane 1 2063 Weaver St 1 5455 Tree Ridge 1 7135 Flute Lane 1 6384 5th Ridge 1 5499 Flute Ridge 1 6260 5th Lane 1 9109 Britain Drive 1 8442 Britain Hwy 1 7705 Best Ridge 1 2850 Washington St 1 5445 Tree Hwy 1 1126 Texas Hwy 1 7534 MLK Hwy 1 8872 Oak Ridge 1 1515 Pine Lane 1 9169 Pine Ridge 1 6259 Lincoln Hwy 1 8946 2nd Drive 1 5363 Weaver Lane 1 5224 5th Lane 1 4272 Oak Ridge 1 1422 Flute Ave 1 7828 Cherokee Ave 1 3726 MLK Hwy 1 1617 Rock Drive 1 3110 Lincoln Lane 1 9279 Oak Hwy 1 3835 5th Ave 1 4434 Lincoln Ave 1 3654 Cherokee Ave 1 9169 Cherokee Hwy 1 6191 Oak Lane 1 3282 4th Lane 1 4058 Tree Drive 1 4652 Flute Drive 1 2201 4th Lane 1 8991 Embaracadero Ave 1 1687 3rd Lane 1 5022 1st St 1 5855 Apache St 1 3872 5th Drive 1 7819 Oak St 1 7426 Rock Drive 1 7112 Weaver Ave 1 2117 Lincoln Hwy 1 1135 Solo Lane 1 5053 Tree Drive 1 5506 Best St 1 2696 Cherokee Ridge 1 6770 1st St 1 9794 Embaracadero St 1 4905 Francis Ave 1 9879 Apache Drive 1 5483 Francis Drive 1 2048 3rd Ridge 1 2733 Texas Drive 1 8233 Tree Drive 1 2215 Best Ave 1 2942 1st Lane 1 4965 MLK Drive 1 5124 Maple St 1 1598 3rd Drive 1 5667 4th Drive 1 7785 Lincoln Lane 1 2352 MLK Drive 1 3706 Texas Hwy 1 8917 Cherokee Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
fraud_con.incident_severity.value_counts()
Minor Damage 354 Total Loss 280 Major Damage 276 Trivial Damage 90 Name: incident_severity, dtype: int64
fraud_con['incident_severity'] = fraud_con['incident_severity'].astype('category')
fraud_con['incident_severity'] = fraud_con['incident_severity'].cat.codes
fraud_con.incident_severity.value_counts()
1 354 2 280 0 276 3 90 Name: incident_severity, dtype: int64
fraud_con.incident_state.value_counts()
NY 262 SC 248 WV 217 NC 110 VA 110 PA 30 OH 23 Name: incident_state, dtype: int64
fraud_con['incident_state'] = fraud_con['incident_state'].astype('category')
fraud_con['incident_state'] = fraud_con['incident_state'].cat.codes
fraud_con.incident_state.value_counts()
1 262 4 248 6 217 5 110 0 110 3 30 2 23 Name: incident_state, dtype: int64
for column in fraud_con.columns:
if fraud_con[column].dtype=='object':
print(column.upper(),':',fraud_con[column].nunique())
print(fraud_con[column].value_counts().sort_values())
print('\n')
POLICY_BIND_DATE : 951 06-09-2005 1 30-04-2002 1 19-05-2014 1 21-05-2010 1 09-08-1993 1 12-10-2002 1 20-06-2002 1 28-04-2013 1 15-04-2013 1 01-03-2012 1 19-01-1998 1 28-07-2008 1 04-12-1995 1 20-02-2009 1 28-04-1994 1 24-05-2001 1 19-09-1991 1 20-07-1990 1 13-06-1994 1 10-01-2012 1 08-03-1995 1 10-02-1998 1 19-07-2006 1 11-01-2009 1 12-01-2012 1 03-09-2008 1 11-07-1991 1 29-08-2010 1 09-06-2001 1 05-07-2007 1 06-06-2002 1 05-07-2000 1 26-11-2001 1 05-02-2006 1 27-04-2009 1 08-07-2001 1 05-06-2011 1 23-04-2008 1 11-08-2007 1 15-11-2000 1 17-02-2010 1 01-03-2002 1 03-05-1991 1 17-11-2009 1 05-05-1998 1 10-12-2014 1 13-06-1992 1 29-05-1999 1 05-12-2014 1 25-04-2014 1 03-12-2005 1 05-12-1995 1 21-02-2006 1 27-10-2013 1 16-10-2002 1 12-10-1994 1 20-02-1998 1 05-02-1991 1 18-10-2005 1 18-11-2011 1 27-02-1994 1 22-03-2000 1 08-01-1994 1 08-06-1994 1 18-12-1994 1 19-03-1992 1 29-08-1995 1 06-06-2001 1 12-01-2010 1 29-03-2004 1 13-09-2003 1 08-04-1991 1 26-03-1995 1 15-07-1990 1 12-11-2009 1 01-02-1990 1 11-05-1996 1 23-06-2000 1 17-06-2009 1 24-04-1995 1 27-09-1990 1 03-03-1997 1 05-11-2008 1 02-09-2014 1 21-04-2003 1 03-11-1996 1 17-08-2014 1 15-09-1990 1 31-10-1997 1 03-10-1994 1 30-12-2002 1 06-03-2005 1 20-05-1995 1 16-07-1998 1 11-12-1992 1 09-04-1999 1 19-03-2014 1 15-12-1996 1 25-01-1999 1 08-02-1991 1 05-09-1996 1 10-11-2005 1 10-06-2014 1 18-11-1994 1 05-02-1997 1 30-08-2014 1 25-11-2009 1 12-09-1994 1 23-10-1996 1 13-11-2002 1 19-05-1990 1 19-04-2002 1 14-02-1994 1 16-09-1990 1 18-08-1990 1 15-02-1990 1 18-10-2012 1 18-06-2011 1 05-01-2012 1 23-08-2003 1 22-12-1995 1 09-12-1992 1 14-03-1995 1 21-04-1997 1 09-04-2003 1 15-03-2007 1 17-12-1995 1 03-12-2012 1 12-08-1999 1 15-08-1990 1 08-04-1996 1 18-12-1996 1 17-09-1994 1 04-06-2012 1 30-01-1992 1 18-02-1997 1 12-12-2011 1 23-08-1994 1 17-07-2005 1 10-03-1997 1 31-05-2004 1 25-02-2011 1 09-07-2001 1 01-08-2003 1 07-02-2007 1 05-04-2002 1 10-12-2005 1 18-06-1993 1 08-08-2010 1 18-11-1993 1 24-04-2013 1 28-04-2007 1 29-11-2009 1 11-06-2014 1 26-03-1990 1 20-06-2012 1 28-06-2010 1 16-07-1995 1 29-11-2004 1 29-09-2005 1 21-04-2010 1 28-03-1990 1 09-02-1993 1 04-04-2003 1 14-11-2014 1 08-02-2009 1 29-10-1991 1 01-02-1998 1 22-06-2009 1 09-05-2009 1 14-01-2005 1 21-04-2014 1 15-07-2000 1 30-09-1991 1 02-01-2004 1 04-12-2007 1 10-12-1993 1 11-05-1997 1 14-10-1992 1 02-10-2003 1 11-01-2010 1 25-01-2006 1 25-06-2002 1 20-11-2002 1 21-08-1991 1 03-06-2014 1 18-12-2013 1 12-10-1998 1 12-07-2013 1 29-06-2006 1 17-06-2005 1 05-10-1999 1 09-04-2001 1 13-10-1990 1 13-04-2006 1 02-06-2012 1 06-09-1995 1 11-09-1997 1 26-08-2004 1 08-12-2001 1 24-10-2004 1 24-09-1994 1 25-11-1994 1 01-10-2010 1 17-02-2003 1 16-12-2007 1 22-06-1990 1 10-02-1993 1 30-07-2003 1 17-07-1995 1 17-08-1994 1 27-09-2010 1 08-05-2001 1 23-05-1999 1 17-02-1995 1 13-09-2002 1 30-06-2004 1 04-04-1992 1 16-11-2004 1 18-07-2002 1 09-03-2007 1 08-01-1990 1 02-09-1996 1 06-05-2002 1 12-03-1994 1 11-12-1994 1 10-10-1993 1 10-11-1992 1 20-01-2014 1 16-09-1997 1 13-11-2014 1 12-04-2013 1 05-08-1993 1 06-08-1999 1 14-05-2006 1 28-10-1999 1 11-02-2003 1 17-09-2014 1 02-12-2011 1 25-02-2008 1 08-08-1994 1 25-05-2002 1 25-04-1991 1 06-05-1991 1 04-01-1996 1 18-02-2000 1 21-12-2006 1 22-05-1999 1 14-06-2004 1 18-10-1996 1 04-12-2005 1 27-07-2005 1 23-12-2013 1 04-05-1995 1 14-11-1999 1 29-05-2003 1 13-04-2002 1 02-10-1990 1 05-08-2012 1 28-04-2005 1 20-07-2008 1 15-03-1992 1 03-11-1991 1 18-03-2008 1 24-09-1992 1 01-12-2008 1 09-08-2002 1 09-02-2000 1 18-02-1995 1 07-06-1994 1 16-06-1993 1 05-07-2009 1 10-10-2005 1 15-06-2004 1 25-12-2001 1 05-08-2014 1 07-04-1994 1 30-12-2011 1 18-06-1998 1 10-09-1998 1 25-07-2011 1 03-10-2000 1 18-02-1999 1 25-08-2011 1 26-06-2001 1 16-03-2008 1 14-01-2008 1 03-02-1990 1 21-07-2008 1 10-04-1992 1 07-09-2006 1 09-08-2003 1 21-10-2009 1 23-07-2009 1 04-01-2009 1 18-08-1996 1 03-09-2013 1 04-02-1992 1 19-12-1998 1 08-08-2013 1 05-12-2013 1 14-01-1998 1 01-02-2011 1 07-11-2008 1 29-05-1995 1 15-11-2004 1 09-10-2012 1 26-01-1996 1 20-10-1993 1 25-06-2011 1 24-03-2007 1 22-11-2012 1 16-11-2000 1 17-08-1991 1 06-10-2012 1 22-03-1992 1 12-04-2002 1 07-04-2005 1 02-06-2002 1 21-05-2000 1 11-07-2007 1 17-02-2009 1 30-04-2007 1 19-12-1997 1 17-03-1990 1 11-11-1994 1 07-09-1999 1 16-02-2001 1 28-10-2008 1 30-03-1995 1 15-05-2000 1 23-01-1996 1 23-02-1990 1 20-08-1990 1 06-06-1993 1 18-02-2007 1 11-11-1996 1 03-07-1992 1 10-07-2012 1 03-03-2007 1 15-06-2014 1 27-06-2005 1 05-12-2009 1 08-01-2013 1 27-05-2003 1 21-02-1995 1 11-04-2005 1 23-01-2002 1 18-01-2003 1 29-01-1993 1 07-03-2008 1 26-01-1994 1 14-03-1990 1 19-04-2009 1 01-07-2005 1 08-07-1996 1 16-06-2003 1 20-09-1993 1 28-05-2014 1 19-04-2006 1 30-11-1996 1 25-05-2011 1 15-10-1996 1 21-03-1998 1 10-01-2004 1 09-01-1999 1 28-10-1997 1 28-03-2001 1 13-07-2013 1 09-12-2001 1 05-08-1997 1 03-11-2009 1 21-11-1991 1 06-03-2004 1 21-01-2002 1 02-08-1990 1 14-02-1996 1 28-01-2003 1 29-04-1990 1 12-10-2006 1 28-03-2003 1 11-03-2007 1 02-10-1992 1 04-11-1991 1 29-03-1995 1 27-06-2006 1 17-08-2011 1 22-01-2011 1 04-01-2003 1 24-05-2006 1 30-10-1997 1 14-10-2005 1 01-05-1999 1 19-06-1996 1 15-08-2000 1 07-04-1992 1 26-05-2002 1 05-05-1993 1 27-05-1994 1 22-11-2008 1 24-12-2006 1 05-08-2006 1 27-05-2002 1 30-04-2006 1 18-03-2003 1 27-11-1990 1 24-04-2012 1 21-12-1999 1 06-06-1992 1 06-05-2014 1 03-03-1992 1 12-09-2001 1 06-06-2011 1 15-07-2004 1 15-01-1992 1 21-12-1996 1 03-08-2010 1 07-01-2005 1 02-05-2007 1 31-08-2014 1 04-02-2004 1 11-06-2008 1 23-11-1997 1 19-08-1995 1 06-12-1993 1 08-02-1996 1 05-10-1991 1 26-09-1991 1 13-05-2001 1 10-05-2009 1 27-10-2001 1 08-05-1995 1 06-03-2003 1 01-07-2013 1 03-08-2009 1 27-07-1997 1 29-05-1992 1 07-12-1998 1 01-03-2010 1 26-02-2005 1 21-04-2006 1 26-08-2000 1 07-04-1998 1 31-10-1993 1 19-04-2007 1 12-10-1995 1 19-09-2004 1 20-12-1990 1 11-02-1991 1 13-11-1998 1 07-11-1994 1 19-06-2008 1 25-10-2012 1 04-03-2001 1 29-07-2012 1 17-01-2015 1 19-09-2012 1 14-07-2000 1 21-06-1994 1 13-03-2008 1 11-12-2009 1 23-10-2007 1 07-04-2014 1 22-02-1992 1 26-10-1996 1 12-12-1998 1 04-07-2007 1 19-05-1992 1 16-01-1996 1 27-01-2007 1 19-10-1992 1 24-08-2000 1 18-10-2000 1 01-06-2006 1 19-12-2004 1 27-11-2005 1 27-09-2011 1 11-07-2002 1 21-11-2006 1 02-04-1991 1 28-08-1995 1 04-07-2013 1 18-02-1990 1 08-04-2003 1 02-01-2001 1 12-12-1999 1 17-06-2006 1 05-01-2013 1 18-01-2014 1 17-12-2003 1 10-04-2013 1 05-03-2009 1 12-12-1991 1 31-03-2005 1 25-08-2012 1 22-10-2011 1 09-01-2002 1 14-11-2010 1 30-01-2005 1 28-02-2004 1 30-08-2000 1 05-08-1996 1 31-07-2011 1 09-12-2006 1 28-08-2005 1 22-02-1994 1 28-01-1998 1 01-11-1991 1 20-11-2005 1 08-03-1998 1 20-03-1992 1 15-08-2011 1 27-01-1991 1 20-08-1996 1 07-12-1992 1 14-01-1999 1 20-11-1997 1 28-10-2006 1 17-11-2004 1 07-10-1990 1 28-12-1998 1 05-07-2001 1 05-02-1994 1 04-03-2000 1 30-07-1992 1 13-02-1991 1 06-03-2010 1 03-06-2009 1 30-11-1993 1 08-07-1990 1 25-04-2002 1 01-09-1992 1 15-04-2004 1 11-03-2014 1 07-04-2010 1 16-05-2001 1 20-09-1999 1 24-01-2010 1 22-07-2004 1 25-11-2006 1 12-02-1998 1 21-08-1994 1 02-09-2011 1 01-05-2010 1 19-09-2009 1 19-10-2001 1 31-12-2003 1 11-02-1994 1 31-10-2013 1 02-02-1996 1 06-02-2009 1 16-11-2007 1 07-01-2011 1 27-08-2014 1 19-10-1999 1 07-07-1997 1 08-09-2009 1 10-09-2002 1 23-06-2012 1 20-07-2007 1 25-11-2002 1 08-03-2009 1 05-07-1999 1 17-05-1994 1 27-12-2000 1 15-02-1993 1 16-09-2009 1 26-10-2012 1 16-03-1998 1 24-06-2014 1 02-10-1997 1 24-09-2001 1 23-07-1992 1 30-06-2002 1 31-07-1999 1 07-12-2001 1 04-01-2002 1 28-11-1991 1 11-07-1996 1 17-06-2008 1 21-12-2013 1 10-09-2000 1 16-12-2011 1 03-10-2014 1 10-04-2009 1 28-07-2002 1 21-03-1997 1 06-09-2008 1 29-06-2002 1 07-08-1991 1 19-01-1996 1 24-04-2007 1 28-09-2011 1 12-10-1997 1 25-01-2008 1 08-06-2005 1 20-04-2004 1 27-02-2010 1 29-11-1999 1 23-08-2005 1 14-05-2001 1 03-04-2001 1 22-01-1993 1 05-01-2014 1 06-12-1991 1 29-04-2010 1 06-11-2013 1 29-02-1992 1 06-12-1995 1 09-08-1990 1 28-10-1998 1 07-11-1992 1 02-12-2012 1 13-04-2003 1 07-11-2012 1 13-12-2014 1 21-10-2005 1 19-06-1992 1 27-06-2008 1 11-10-2012 1 11-12-1998 1 02-11-2010 1 20-08-1994 1 03-11-1990 1 06-06-2014 1 01-05-1997 1 02-01-1991 1 01-01-2008 1 19-10-1990 1 11-08-1999 1 22-07-1999 1 30-08-2012 1 02-06-2010 1 21-02-1999 1 18-08-2006 1 18-01-1997 1 04-12-2013 1 24-05-2003 1 06-01-2011 1 05-01-1991 1 10-03-1991 1 16-08-2003 1 29-10-2006 1 04-08-1997 1 10-02-1997 1 26-08-2013 1 29-06-2009 1 15-08-1999 1 10-05-2012 1 27-06-1996 1 01-04-2002 1 14-11-2005 1 03-08-1993 1 23-03-2009 1 25-07-1995 1 16-03-2014 1 31-01-2011 1 16-06-1991 1 14-04-1993 1 15-07-2011 1 29-07-1995 1 16-09-2013 1 21-01-2009 1 25-04-2007 1 11-10-1994 1 20-04-2013 1 12-05-2007 1 02-02-2001 1 13-10-1991 1 03-09-1991 1 25-12-1997 1 14-02-1992 1 10-03-2004 1 29-09-2001 1 13-11-1995 1 22-04-2010 1 21-08-2010 1 24-10-1997 1 09-11-1991 1 22-02-2015 1 16-04-1995 1 18-06-2000 1 01-10-2001 1 24-07-2012 1 02-08-1992 1 17-03-2010 1 06-12-1996 1 10-08-2006 1 03-02-1994 1 03-02-1993 1 15-08-2002 1 05-12-2007 1 09-02-2007 1 08-02-1990 1 01-10-2006 1 16-03-2003 1 30-03-1999 1 14-02-1990 1 01-08-2010 1 20-01-1996 1 29-12-2010 1 30-04-2013 1 23-07-2014 1 09-11-1992 1 25-03-2007 1 16-06-2006 1 11-03-1996 1 29-10-1993 1 02-11-2012 1 03-06-1991 1 08-06-2002 1 18-06-2010 1 24-07-2007 1 18-06-1997 1 30-09-1993 1 28-03-1995 1 02-08-2006 1 28-08-1998 1 20-11-1991 1 25-12-1991 1 23-08-2009 1 24-07-1999 1 18-08-2007 1 18-02-2011 1 02-04-2002 1 05-05-1991 1 02-08-1991 1 10-04-1994 1 26-09-2010 1 29-02-2004 1 31-12-2012 1 25-02-2005 1 07-03-2005 1 01-11-2014 1 06-08-1994 1 12-08-2004 1 17-09-2003 1 27-12-2001 1 16-01-2013 1 24-12-2012 1 27-04-2012 1 29-11-2001 1 04-03-2014 1 21-08-2006 1 18-03-1990 1 03-04-2013 1 06-12-2006 1 23-07-1996 1 28-09-2007 1 20-11-2010 1 17-09-2011 1 20-11-2008 1 01-03-1991 1 13-08-2004 1 24-06-1998 1 30-08-1997 1 04-02-2011 1 17-04-2005 1 24-03-2006 1 28-03-2006 1 25-11-1991 1 13-01-1991 1 27-01-2000 1 08-10-1995 1 09-05-2008 1 20-02-2001 1 24-02-2012 1 13-12-1993 1 23-01-2003 1 22-07-2002 1 03-02-2008 1 04-06-1996 1 21-09-1990 1 13-12-1995 1 28-11-2002 1 18-05-1993 1 14-03-2012 1 28-01-1993 1 10-02-2002 1 05-11-2000 1 27-11-1992 1 18-10-2006 1 01-12-2000 1 03-02-2006 1 24-10-2007 1 19-09-2007 1 28-02-2010 1 11-10-1992 1 16-11-1991 1 04-03-2006 1 16-07-2014 1 28-02-1997 1 28-10-2007 1 17-07-2012 1 16-07-1991 1 03-08-1994 1 29-07-1996 1 28-09-1994 1 04-11-2004 1 28-07-2014 1 05-08-2005 1 29-07-2009 1 21-04-2001 1 10-02-2008 1 22-09-2002 1 04-11-2010 1 14-11-2007 1 01-06-1997 1 23-02-2014 1 24-05-2004 1 25-11-2004 1 10-02-1994 1 04-03-2003 1 15-02-2011 1 01-05-2013 1 03-01-2015 1 23-01-2013 1 11-09-2004 1 25-08-1990 1 26-06-2009 1 13-04-1991 1 28-12-1999 1 11-07-2001 1 09-11-2008 1 01-04-1994 1 21-04-2008 1 22-12-2012 1 20-05-1990 1 11-09-2010 1 17-04-1999 1 07-07-2013 1 06-02-2006 1 24-01-2009 1 30-06-2000 1 21-06-1997 1 29-03-2005 1 04-02-2013 1 16-05-1990 1 27-01-1990 1 28-12-2004 1 29-09-1997 1 07-05-1990 1 18-09-1999 1 08-05-2005 1 11-05-2010 1 02-08-2009 1 15-10-2005 1 26-01-2011 1 10-04-1996 1 23-04-1995 1 18-10-1995 1 16-12-1998 1 18-07-1991 1 14-09-2001 1 09-03-1999 1 24-06-2003 1 26-08-2012 1 01-03-1997 1 19-05-1993 1 06-07-1996 1 09-12-2005 1 22-09-1990 1 29-08-1999 1 06-11-2001 1 07-08-1994 1 11-02-2010 1 12-07-2006 1 06-10-2002 1 19-12-2011 1 10-06-2001 1 15-02-2001 1 23-08-1996 1 07-08-1992 1 05-07-2003 1 10-02-2007 1 28-12-2014 1 17-10-2014 1 21-07-1996 1 02-07-1991 1 07-01-2008 1 11-10-2003 1 13-02-2003 1 26-06-2000 1 16-07-2004 1 04-03-2007 1 09-10-2009 1 09-07-2013 1 14-03-2007 1 07-12-1993 1 12-02-2009 1 23-02-1993 1 28-01-1992 1 16-07-2003 1 24-06-2009 1 04-07-1997 1 18-04-1993 1 21-03-2008 1 03-02-1999 1 06-09-2000 1 11-11-2013 1 09-10-1995 1 25-10-2007 1 09-10-1990 1 20-03-1991 1 19-12-2001 1 08-11-2009 2 27-07-2014 2 25-12-2013 2 11-03-2010 2 15-05-1997 2 16-05-2008 2 28-12-2002 2 16-07-2002 2 20-07-1991 2 07-12-1995 2 04-06-2000 2 03-02-1997 2 28-01-2010 2 21-09-1996 2 09-08-2004 2 14-12-1991 2 04-05-2000 2 05-01-1992 2 19-09-1995 2 03-01-2004 2 07-04-1999 2 30-08-1993 2 28-12-1991 2 09-07-2002 2 29-09-1999 2 07-12-1999 2 21-09-2005 2 25-09-2001 2 05-07-2014 2 11-11-1998 2 15-11-1997 2 09-03-2003 2 20-09-1990 2 25-05-1990 2 07-07-1996 2 24-06-1990 2 21-12-2002 2 06-05-2007 2 14-07-1997 2 07-11-1997 2 22-08-1991 2 14-04-1992 2 29-01-1998 2 05-08-1992 3 28-04-1992 3 01-01-2006 3 Name: policy_bind_date, dtype: int64 INCIDENT_DATE : 60 05-01-2015 7 11-01-2015 9 07-02-2015 10 25-01-2015 10 11-02-2015 10 10-02-2015 10 19-02-2015 10 29-01-2015 11 26-01-2015 11 02-01-2015 11 04-01-2015 12 01-03-2015 12 27-01-2015 13 23-01-2015 13 03-02-2015 13 09-02-2015 13 20-02-2015 14 27-02-2015 14 22-01-2015 14 15-01-2015 15 17-01-2015 15 28-01-2015 15 18-02-2015 15 15-02-2015 16 16-01-2015 16 16-02-2015 16 05-02-2015 16 13-02-2015 16 06-01-2015 17 09-01-2015 17 24-02-2015 17 08-02-2015 17 26-02-2015 17 20-01-2015 18 28-02-2015 18 14-02-2015 18 18-01-2015 18 03-01-2015 18 25-02-2015 18 01-02-2015 18 14-01-2015 19 01-01-2015 19 21-01-2015 19 12-01-2015 19 23-02-2015 19 21-02-2015 19 31-01-2015 20 12-02-2015 20 22-02-2015 20 06-02-2015 20 30-01-2015 21 13-01-2015 21 08-01-2015 22 19-01-2015 23 10-01-2015 24 04-02-2015 24 24-01-2015 24 07-01-2015 25 17-02-2015 26 02-02-2015 28 Name: incident_date, dtype: int64 INCIDENT_LOCATION : 1000 3340 3rd Hwy 1 3457 Texas Lane 1 9103 MLK Lane 1 1507 Solo Ave 1 4653 Pine St 1 6684 Solo Lane 1 9580 MLK Ave 1 3006 Lincoln Ridge 1 1358 Maple St 1 3029 5th Ave 1 2217 Tree Lane 1 4335 1st St 1 5189 Francis Drive 1 1553 Lincoln St 1 3660 Andromedia Hwy 1 5812 Weaver Ave 1 5499 Elm Hwy 1 4567 Pine Ave 1 7511 1st Ave 1 4710 Lincoln Hwy 1 5778 Pine Ridge 1 4143 Maple Ridge 1 4175 Elm Ridge 1 8281 Lincoln Lane 1 1725 Solo Lane 1 3929 Elm Ave 1 9787 Andromedia Ave 1 5894 Flute Drive 1 2100 Francis Drive 1 1298 Maple Hwy 1 2774 Apache Drive 1 8617 Best Ave 1 9720 Lincoln Hwy 1 3495 Britain Drive 1 6359 MLK Ridge 1 1331 Elm Ridge 1 6655 5th Drive 1 9988 Rock Ridge 1 6582 Elm Lane 1 2290 4th Ave 1 1529 Elm Ridge 1 5678 Lincoln Drive 1 9397 5th Hwy 1 1562 Britain St 1 9439 MLK St 1 7240 5th Ridge 1 5790 Flute Ridge 1 2873 Flute Ave 1 3592 MLK Ridge 1 3221 Solo Ridge 1 4239 Weaver Ave 1 7523 Oak Lane 1 2889 Francis St 1 9523 Solo Hwy 1 6375 2nd Lane 1 7314 Tree Drive 1 9278 Francis Ridge 1 5201 Texas Hwy 1 7601 Andromedia Lane 1 2644 Elm Drive 1 4972 Francis Lane 1 2123 MLK Ridge 1 2204 Washington Lane 1 9239 Washington Ridge 1 7846 Andromedia Drive 1 9760 Solo Lane 1 2577 Texas Ridge 1 8876 1st St 1 5028 Maple Ridge 1 9105 Tree Lane 1 2654 Embaracadero St 1 6834 1st Drive 1 3553 Texas Ave 1 6399 Oak Drive 1 6179 3rd Ridge 1 5821 2nd St 1 7628 4th Lane 1 7488 Lincoln Lane 1 4857 Weaver St 1 2920 5th Ave 1 8118 Elm Ridge 1 8576 Andromedia St 1 5104 Francis Drive 1 3929 Oak Drive 1 1989 Solo Lane 1 8864 Tree Ridge 1 6250 1st Ridge 1 2654 Elm Drive 1 1655 Francis Hwy 1 3926 Rock Lane 1 4642 Rock Ridge 1 9214 Texas Drive 1 6149 Best Ridge 1 4814 Lincoln Lane 1 5071 Flute Ridge 1 1833 Solo Ave 1 4232 Britain Ridge 1 5783 Oak Ave 1 4939 Best St 1 6956 Maple Drive 1 2968 Andromedia Ave 1 1364 Best St 1 6618 Cherokee Drive 1 6092 5th Ave 1 5474 Weaver Hwy 1 1580 Maple Lane 1 8269 Sky Hwy 1 2324 Texas Ridge 1 4546 Tree St 1 4964 Elm Lane 1 5994 5th Ave 1 8014 Embaracadero Drive 1 4154 Lincoln Hwy 1 3966 Francis Ridge 1 5838 Pine Lane 1 6678 Weaver Drive 1 3884 Pine Lane 1 8211 Sky Hwy 1 9418 5th Hwy 1 5071 1st Lane 1 6378 Britain Ave 1 5971 5th Hwy 1 1173 Andromedia Ave 1 6158 Sky Ridge 1 4633 5th Lane 1 1437 3rd Lane 1 7900 Sky Hwy 1 4699 Texas Ridge 1 8080 Oak Lane 1 9214 Elm Ridge 1 8214 Flute St 1 1679 2nd Hwy 1 2272 Embaracadero Drive 1 1328 Texas Lane 1 2275 Best Lane 1 8100 3rd Ave 1 3567 4th Drive 1 3998 Flute St 1 3540 Maple St 1 6309 Cherokee Ave 1 3311 2nd Drive 1 8336 1st Ridge 1 3796 Cherokee Drive 1 6550 Andromedia St 1 6751 5th Hwy 1 8983 Tree St 1 3416 Washington Drive 1 9038 2nd Lane 1 3087 Oak Hwy 1 7756 Solo Drive 1 1824 5th Lane 1 1316 Britain Ridge 1 7583 Washington Ave 1 7098 Lincoln Hwy 1 1267 Francis Hwy 1 7825 1st Ridge 1 7721 Washington Ridge 1 5279 Pine Ridge 1 8492 Andromedia Ridge 1 6957 Weaver Drive 1 3618 Sky Ave 1 6408 Weaver Ridge 1 7630 Rock Drive 1 7253 MLK St 1 4866 4th Hwy 1 3770 Flute Drive 1 6981 Weaver St 1 4492 Andromedia Ave 1 6440 Rock Lane 1 1353 Washington St 1 4486 Cherokee Ridge 1 7575 Pine St 1 3220 Rock Drive 1 2725 Britain Ridge 1 6409 Cherokee Drive 1 7649 Texas St 1 9818 Cherokee Ave 1 8538 Texas Lane 1 6206 3rd Ridge 1 3184 Oak Ave 1 9685 Sky Ridge 1 5771 Best St 1 7434 Oak Hwy 1 7644 Tree Ridge 1 2526 Embaracadero Ave 1 7061 Cherokee Drive 1 1102 Apache Hwy 1 2862 Tree Ridge 1 6723 Best Drive 1 1248 MLK Ridge 1 3982 Washington Hwy 1 5806 Embaracadero St 1 9067 Texas Ave 1 6012 Texas Hwy 1 1821 Andromedia Ridge 1 6619 Flute Ave 1 7295 Tree Hwy 1 3320 5th Hwy 1 2603 Andromedia Hwy 1 6874 Maple Ridge 1 4862 Lincoln Hwy 1 3419 Apache St 1 5608 Solo St 1 9240 Britain Ave 1 1416 Cherokee Ridge 1 4672 MLK St 1 9821 Francis Ave 1 2193 4th Ridge 1 8689 Maple Hwy 1 7859 4th Ridge 1 7928 Maple Ridge 1 5211 Weaver Drive 1 4907 Andromedia Drive 1 5639 1st Ridge 1 9706 MLK Lane 1 4538 Flute Hwy 1 9633 MLK Lane 1 7709 Rock Lane 1 6939 3rd Hwy 1 2502 Apache Hwy 1 7937 Weaver Ridge 1 2457 Washington Ave 1 7773 Tree Hwy 1 9373 Pine Hwy 1 3492 Flute Lane 1 7082 Oak Ridge 1 8822 Sky St 1 9911 Britain Lane 1 9728 Britain Hwy 1 6058 Andromedia Hwy 1 4460 4th Lane 1 5719 2nd Lane 1 9177 Texas Ave 1 9730 2nd Hwy 1 6985 Maple Lane 1 6467 Best Ave 1 6838 Flute Lane 1 1810 Elm Hwy 1 2500 Tree St 1 6971 Best Ridge 1 6859 Flute Ridge 1 8404 Embaracadero St 1 9633 4th St 1 2469 Francis Lane 1 2492 Lincoln Lane 1 1830 Sky St 1 2220 1st Lane 1 6317 Best St 1 6848 Elm Hwy 1 8917 Tree Ridge 1 2950 MLK Ave 1 7576 Pine Ridge 1 9744 Texas Drive 1 1110 4th Drive 1 5312 Francis Ridge 1 6530 Weaver Ave 1 4955 Lincoln Ridge 1 1365 Francis Ave 1 4237 4th St 1 6604 Apache Drive 1 7797 Tree Ridge 1 8758 5th St 1 8907 Tree Ave 1 2798 1st Ave 1 9417 Tree Hwy 1 2333 Maple Lane 1 4618 Flute Ave 1 8906 Elm Lane 1 4755 Best Lane 1 5846 Weaver Drive 1 2005 Texas Hwy 1 6259 Weaver St 1 1491 Francis Ridge 1 2381 1st Hwy 1 4614 MLK Ave 1 9360 3rd Drive 1 2037 5th Drive 1 2352 Sky Drive 1 7877 3rd Ridge 1 8742 4th St 1 6738 Washington Hwy 1 5780 4th Ave 1 3039 Oak Hwy 1 7897 Lincoln St 1 8456 1st Ave 1 6706 Francis Drive 1 1320 Flute Lane 1 1840 Embaracadero Ave 1 9138 1st St 1 8212 Flute Ridge 1 8548 Cherokee Ridge 1 7791 Britain Ridge 1 9935 4th Drive 1 6355 4th Hwy 1 9588 Solo St 1 6068 2nd St 1 3167 4th Ridge 1 5333 MLK Lane 1 3028 5th St 1 8493 Apache Drive 1 9070 Tree Ave 1 1956 Apache St 1 8198 Embaracadero Lane 1 1331 Britain Hwy 1 8782 3rd St 1 9737 Solo Hwy 1 5418 Britain Ave 1 5663 Oak Lane 1 3841 Washington Lane 1 6934 Lincoln Ave 1 2533 Elm St 1 3915 Embaracadero St 1 3834 Pine St 1 5475 Rock Lane 1 6889 Cherokee St 1 7466 MLK Ridge 1 4872 Rock Ridge 1 8834 Elm Drive 1 5341 5th Ave 1 5904 1st Drive 1 2376 Sky Ridge 1 7108 Tree St 1 7380 5th Hwy 1 3936 Tree Drive 1 6451 1st Hwy 1 5650 Sky Drive 1 6668 Andromedia Ridge 1 4625 MLK Drive 1 1469 Lincoln Drive 1 3998 4th Hwy 1 1951 Best Ave 1 8368 Cherokee Ave 1 8624 Francis Ave 1 4394 Oak St 1 8991 Texas Hwy 1 7930 Texas Ave 1 2617 Andromedia Drive 1 8006 Maple Hwy 1 9639 Britain Ridge 1 2820 Britain St 1 4519 Embaracadero St 1 7327 Lincoln Drive 1 2123 Texas Ave 1 8453 Elm St 1 3753 Francis Lane 1 6137 MLK St 1 8949 Rock Hwy 1 4475 Lincoln Ridge 1 1546 Cherokee Ave 1 7571 Elm Ridge 1 5168 5th Ave 1 7973 4th St 1 4119 Texas St 1 3797 Solo Lane 1 4885 Oak Lane 1 1919 4th Lane 1 9082 3rd Lane 1 4434 Weaver St 1 1515 Embaracadero St 1 7042 Maple Ridge 1 3952 Andromedia Lane 1 3925 Sky St 1 2299 1st St 1 6751 Pine Ridge 1 8203 Lincoln Ave 1 3697 Apache Drive 1 3818 Texas Ridge 1 1123 5th Lane 1 4242 Rock Lane 1 4702 Texas Drive 1 3656 Solo Ave 1 9286 Oak Ave 1 8701 5th Lane 1 4981 Weaver St 1 8306 1st Drive 1 7745 Washington Ridge 1 1386 Britain St 1 5969 Francis St 1 7168 Andromedia Ridge 1 1472 4th Drive 1 6676 Tree Lane 1 8941 Solo Ridge 1 1186 Rock St 1 2537 5th Ave 1 2804 Best St 1 6581 Rock Ridge 1 1275 4th Ridge 1 9942 Tree Ave 1 3196 Cherokee St 1 2318 Washington Hwy 1 3376 5th Drive 1 6851 3rd Drive 1 2865 Maple Lane 1 4347 2nd Ridge 1 9682 Cherokee Ridge 1 1213 4th Lane 1 9352 Washington Ave 1 6256 Elm St 1 5226 Maple St 1 1910 Sky Ave 1 1815 Cherokee Drive 1 4574 Britain Hwy 1 2280 4th Ave 1 6888 Elm Ridge 1 7397 4th Drive 1 2757 4th Hwy 1 8078 Britain Hwy 1 3127 Flute St 1 1346 5th Lane 1 1532 Washington St 1 6501 5th Drive 1 8940 Elm Ave 1 5649 Texas Ave 1 9719 4th Lane 1 1273 Rock Lane 1 3842 Solo Ridge 1 8766 Lincoln Lane 1 7236 Apache Lane 1 7544 Washington Ave 1 8096 Apache Hwy 1 3525 3rd Hwy 1 4826 5th St 1 9224 Sky Drive 1 4020 Best Drive 1 3809 Texas Lane 1 7495 Washington Ave 1 9020 Elm Ave 1 2808 Elm St 1 6492 4th Lane 1 2003 Maple Hwy 1 3618 Maple Lane 1 9657 5th Ave 1 8805 Cherokee Drive 1 3061 Francis Hwy 1 8621 Best Ridge 1 6605 Tree Ave 1 9573 Weaver Ave 1 8101 3rd Ridge 1 7460 Apache Lane 1 5318 5th Ave 1 8638 3rd Ave 1 7693 Cherokee Lane 1 3066 Francis Ave 1 4835 Britain Ridge 1 4629 Elm Ridge 1 1705 Weaver St 1 6309 5th Ave 1 2397 Cherokee Ave 1 6658 Weaver St 1 9742 5th Ridge 1 9034 Weaver Ridge 1 8381 Solo Hwy 1 9316 Pine Ave 1 5630 1st Drive 1 5997 Embaracadero Drive 1 8832 Pine Drive 1 6677 Andromedia Drive 1 4994 Lincoln Drive 1 9847 Elm St 1 5602 Britain St 1 1809 Sky St 1 1555 Washington Lane 1 1916 Elm St 1 7782 Rock St 1 5532 Francis Lane 1 6741 Oak Ridge 1 7693 Britain Lane 1 1128 Maple Lane 1 5280 Pine Ave 1 5532 Weaver Ridge 1 9816 Britain St 1 8204 Pine Lane 1 4627 Elm Ridge 1 3092 Texas Drive 1 1914 Francis St 1 5925 Tree Hwy 1 3397 5th Ave 1 7281 Oak St 1 4447 Francis Hwy 1 1845 Best St 1 1325 1st Lane 1 2697 Oak Drive 1 9724 Maple St 1 7121 Britain Drive 1 8542 Lincoln Ridge 1 5061 Francis Ave 1 2777 Solo Drive 1 6493 Lincoln Lane 1 1953 Sky Lane 1 1618 Maple Hwy 1 1371 Texas Lane 1 6193 1st Hwy 1 3653 Elm Drive 1 4429 Washington St 1 3707 Oak Ridge 1 3664 Francis Ridge 1 8667 Weaver Lane 1 7477 MLK Drive 1 2903 Weaver Drive 1 7582 Pine Drive 1 4369 Maple Lane 1 5191 4th St 1 4585 Francis Ave 1 4176 Britain Hwy 1 3847 Elm Hwy 1 2832 Andromedia Lane 1 6931 Elm St 1 4755 1st St 1 2199 Texas Drive 1 7976 Britain Drive 1 8489 Pine Hwy 1 8983 Francis Ridge 1 4876 Washington Drive 1 7705 Lincoln Drive 1 6516 Solo Drive 1 4214 MLK Ridge 1 4231 3rd Ave 1 1738 Solo Lane 1 5743 4th Ridge 1 8212 Rock Ave 1 2823 Weaver Lane 1 3052 Weaver Ridge 1 7783 Lincoln Hwy 1 6515 Oak Lane 1 5779 2nd Lane 1 8021 Flute Ave 1 3414 Elm Ave 1 3995 Lincoln Hwy 1 7733 Britain Lane 1 9322 Rock Hwy 1 9325 Lincoln Drive 1 1558 1st Ridge 1 3275 Pine St 1 6428 Andromedia Lane 1 2711 Britain Ave 1 9573 2nd Ave 1 3439 Andromedia Hwy 1 7909 Andromedia Hwy 1 7684 Francis Ridge 1 8509 Apache St 1 4554 Sky Ave 1 6435 Texas Ave 1 8845 5th Ave 1 3288 Tree Lane 1 8926 Texas Ridge 1 3799 Embaracadero Drive 1 2756 Britain Hwy 1 5874 1st Hwy 1 7331 Sky Hwy 1 8821 Elm St 1 9422 Washington Ridge 1 2986 MLK Drive 1 9633 Rock Hwy 1 2651 MLK Lane 1 3263 Pine Ridge 1 6583 MLK Ridge 1 6608 Apache Lane 1 1699 Oak Drive 1 6315 2nd Lane 1 1012 5th Lane 1 8954 Apache Lane 1 3102 Apache St 1 3903 Oak Ave 1 4188 Britain Ave 1 3246 Britain Ridge 1 7780 Flute Lane 1 4577 Sky Hwy 1 2430 MLK Ave 1 7281 Maple Hwy 1 8655 Cherokee Lane 1 5584 Britain Lane 1 8964 Francis St 1 8188 Tree Ave 1 6303 1st Drive 1 3693 Pine Ave 1 9878 Washington Ave 1 6011 Britain St 1 6456 Andromedia Drive 1 2311 4th St 1 8215 Flute Drive 1 8639 5th Hwy 1 3751 Tree Hwy 1 6724 Andromedia St 1 7756 Pine Hwy 1 3486 Flute Ave 1 4780 Best Drive 1 5431 3rd Ridge 1 7740 MLK St 1 5459 MLK Ave 1 1240 Tree Lane 1 9489 3rd St 1 7002 Oak Hwy 1 1628 Best Drive 1 6045 Andromedia St 1 1220 MLK Ave 1 7316 Texas Ave 1 5985 Lincoln Lane 1 6128 Elm Lane 1 9153 3rd Hwy 1 8862 Maple Ridge 1 8081 Flute Ridge 1 4098 Weaver Ridge 1 3089 Oak Ridge 1 5771 Sky Ave 1 6603 Francis Hwy 1 8920 Best Ave 1 8492 Weaver Hwy 1 3327 Lincoln Drive 1 4264 Lincoln Ridge 1 1992 Britain Drive 1 1762 Maple Hwy 1 4279 Solo Drive 1 4431 Rock St 1 5839 Weaver Lane 1 2900 Sky Drive 1 2914 Oak Drive 1 1589 Pine St 1 1880 Weaver Drive 1 3900 Texas St 1 4793 4th Ridge 1 9154 MLK Hwy 1 3104 Sky Drive 1 4558 3rd Hwy 1 5051 Elm St 1 1536 Flute Drive 1 5058 4th Lane 1 6681 Texas Ridge 1 4693 Lincoln Hwy 1 6494 4th Ave 1 1269 Flute Drive 1 7574 4th St 1 8879 1st Drive 1 3097 4th Drive 1 6110 Rock Ridge 1 4985 Sky Lane 1 4477 5th Ave 1 2293 Washington Ave 1 4782 Sky Lane 1 7717 Britain Hwy 1 4937 Flute Drive 1 1941 5th Ridge 1 8085 Andromedia St 1 5914 Oak Ave 1 5074 3rd St 1 5383 Maple Drive 1 4053 Sky Lane 1 6853 Sky Hwy 1 5007 Oak St 1 9875 MLK Ave 1 4910 1st Lane 1 7609 Rock St 1 1215 Pine Hwy 1 7299 Apache St 1 6574 4th Drive 1 7877 Sky Lane 1 2078 3rd Ave 1 4390 4th Drive 1 9751 Sky Ridge 1 5765 Washington St 1 5480 3rd Ridge 1 6738 Francis Hwy 1 1388 Embaracadero Hwy 1 3492 Britain St 1 6955 Pine Drive 1 5178 Weaver Hwy 1 5812 3rd Hwy 1 9138 3rd St 1 9317 Apache Ave 1 3878 Tree Lane 1 9748 Sky Drive 1 2644 MLK Drive 1 4905 Best Lane 1 9734 2nd Ridge 1 5622 Best Ridge 1 9751 Tree St 1 7459 Flute St 1 4158 Washington Lane 1 5249 4th Ave 1 3808 5th Ave 1 6044 Weaver Drive 1 7223 Embaracadero St 1 3846 4th Hwy 1 3790 Andromedia Hwy 1 3508 Washington St 1 6719 Flute St 1 9562 4th Ridge 1 3706 4th Hwy 1 3098 Oak Lane 1 9910 Maple Ave 1 1512 Rock Lane 1 3422 Flute St 1 1643 Washington Hwy 1 5621 4th Ave 1 2509 Rock Drive 1 5586 2nd St 1 6634 Texas Ridge 1 9369 Flute Hwy 1 3423 Francis Ave 1 3172 Tree Ridge 1 1030 Pine Lane 1 7142 5th Lane 1 1832 Elm Hwy 1 5093 Flute Lane 1 3171 Andromedia Lane 1 7178 Best Drive 1 8150 Washington Ridge 1 5540 Sky St 1 5585 Washington Drive 1 7121 Rock St 1 1879 4th Lane 1 7066 Texas Ave 1 2886 Tree Ridge 1 9798 Sky Ridge 1 1589 Best Ave 1 3659 Oak Lane 1 4981 Flute Hwy 1 5260 Francis Drive 1 9358 Texas Ridge 1 3289 Britain Drive 1 7819 2nd Ave 1 6067 Weaver Ridge 1 4687 5th Drive 1 3323 1st Lane 1 5160 2nd Hwy 1 4931 Maple Drive 1 7393 Washington St 1 7502 Rock Lane 1 9397 Francis St 1 1923 2nd Hwy 1 3814 Britain Drive 1 3100 Best St 1 1929 Britain Drive 1 4095 MLK St 1 3966 Oak Hwy 1 8973 Washington St 1 5455 Oak Hwy 1 8995 1st Ave 1 5812 Oak St 1 7069 4th Hwy 1 2577 Washington Drive 1 2494 Andromedia Drive 1 8049 4th St 1 1681 Cherokee Hwy 1 4254 Best Ridge 1 5782 Rock Drive 1 7447 Lincoln Ridge 1 4983 MLK Ridge 1 7041 Tree Ridge 1 5640 Embaracadero Lane 1 5380 Pine St 1 7816 MLK Lane 1 7701 Tree St 1 1707 Sky Ave 1 2087 Apache Ave 1 7144 Andromedia St 1 6390 Apache St 1 4539 Texas St 1 5865 Sky Lane 1 4055 2nd Drive 1 2849 Pine Drive 1 2889 Weaver St 1 5277 Texas Lane 1 8668 Flute St 1 8618 Texas Lane 1 3930 Embaracadero St 1 2905 Embaracadero Drive 1 9236 2nd Hwy 1 6443 Washington Ridge 1 9603 Texas Lane 1 6717 Best Drive 1 8167 Apache Ave 1 1376 Pine St 1 6731 Andromedia Hwy 1 4721 Cherokee Hwy 1 2253 Maple Ave 1 9918 Andromedia Drive 1 5276 2nd Lane 1 9007 Francis Hwy 1 5352 Lincoln Drive 1 8245 4th Hwy 1 6660 MLK Drive 1 4545 4th Ridge 1 9101 2nd Hwy 1 8770 1st Lane 1 1957 Washington Ave 1 6608 MLK Hwy 1 7121 Francis Lane 1 6357 Texas Lane 1 4615 Embaracadero Ave 1 7552 3rd St 1 4296 Pine Hwy 1 1218 Sky Hwy 1 3769 Sky St 1 7954 Tree Ridge 1 5862 Apache Ridge 1 3771 4th St 1 1578 5th Lane 1 1985 5th Ave 1 8097 Maple Lane 1 7428 Sky Hwy 1 8602 Washington Ridge 1 8353 Britain Ridge 1 3122 Apache Drive 1 6638 Tree Drive 1 8125 Texas Ridge 1 4414 Solo Drive 1 3777 Maple Ave 1 4268 2nd Ave 1 1229 5th Ave 1 7238 2nd St 1 3227 Maple Ave 1 1306 Andromedia St 1 5901 Elm Drive 1 6702 Andromedia St 1 1381 Francis Ave 1 2299 Britain Drive 1 6945 Texas Hwy 1 8718 Apache Lane 1 3658 Rock Drive 1 6035 Rock Ave 1 7476 4th St 1 3041 3rd Ave 1 9245 Weaver Ridge 1 4107 MLK Ridge 1 6522 Apache Drive 1 6117 4th Ave 1 7923 Elm Ave 1 1741 Best Ridge 1 1806 Weaver Ridge 1 3488 Flute Lane 1 2539 Embaracadero Ridge 1 1133 Apache St 1 1654 Pine St 1 5769 Texas Lane 1 8704 Britain Lane 1 5868 Best Drive 1 6104 Oak Ave 1 2668 Cherokee St 1 9488 Best Drive 1 9856 Apache St 1 7629 5th St 1 6331 MLK Ave 1 8579 Apache Drive 1 2289 Weaver Ridge 1 6429 4th Hwy 1 9081 Cherokee Hwy 1 7535 5th Lane 1 8477 Francis Hwy 1 7201 Washington Ave 1 2230 1st St 1 9610 Cherokee St 1 3743 Andromedia Ridge 1 6479 Francis Ave 1 3418 Texas Lane 1 2086 Francis Drive 1 7197 2nd Drive 1 8459 Apache Ave 1 9043 Maple Hwy 1 9760 4th Hwy 1 4291 Sky Hwy 1 9078 Francis Ridge 1 2003 2nd Hwy 1 8586 1st Ridge 1 1091 1st Drive 1 3053 Lincoln Drive 1 9148 4th Hwy 1 2100 MLK St 1 8809 Flute St 1 1620 Oak Ave 1 7529 Solo Ridge 1 5269 Flute Hwy 1 2337 Lincoln Hwy 1 4453 Best Ave 1 5650 Rock Ave 1 5236 Weaver Drive 1 2878 Britain Hwy 1 2014 Rock Ave 1 9066 Best Ridge 1 1818 Tree St 1 5868 Sky Hwy 1 8064 4th Ave 1 3555 Francis Ridge 1 9611 Pine Ridge 1 4671 5th Ridge 1 6329 Apache Ave 1 9929 Rock Drive 1 4234 Cherokee Lane 1 2646 MLK Drive 1 9484 Pine Drive 1 7500 Texas Ridge 1 3177 MLK Ridge 1 4116 Embaracadero Lane 1 7162 Maple Ave 1 8749 Tree St 1 7570 Cherokee Drive 1 4496 Pine Lane 1 3639 Flute Hwy 1 1028 Sky Lane 1 1454 5th Ridge 1 8957 Weaver Drive 1 1087 Flute Drive 1 6484 Tree Drive 1 3447 Solo Ave 1 2306 5th Lane 1 8811 Maple Hwy 1 7615 Weaver Drive 1 7405 Oak St 1 8524 Pine Lane 1 9529 4th Drive 1 8897 Sky St 1 7504 Flute Drive 1 9293 Pine Lane 1 3550 Washington Ave 1 2753 Cherokee Ave 1 7155 Apache Drive 1 4939 Oak Lane 1 2980 Sky Ridge 1 7551 Britain Lane 1 3805 Lincoln Hwy 1 2787 MLK St 1 5924 Maple Drive 1 6165 Rock Ridge 1 3167 2nd St 1 6536 MLK Hwy 1 1540 Apache Lane 1 7835 Cherokee Hwy 1 3094 Best Lane 1 3982 Weaver Lane 1 4995 Weaver Ridge 1 9980 Lincoln Ave 1 3590 Best Hwy 1 3443 Maple Ridge 1 2809 Francis Lane 1 2063 Weaver St 1 5455 Tree Ridge 1 7135 Flute Lane 1 6384 5th Ridge 1 5499 Flute Ridge 1 6260 5th Lane 1 9109 Britain Drive 1 8442 Britain Hwy 1 7705 Best Ridge 1 2850 Washington St 1 5445 Tree Hwy 1 1126 Texas Hwy 1 7534 MLK Hwy 1 8872 Oak Ridge 1 1515 Pine Lane 1 9169 Pine Ridge 1 6259 Lincoln Hwy 1 8946 2nd Drive 1 5363 Weaver Lane 1 5224 5th Lane 1 4272 Oak Ridge 1 1422 Flute Ave 1 7828 Cherokee Ave 1 3726 MLK Hwy 1 1617 Rock Drive 1 3110 Lincoln Lane 1 9279 Oak Hwy 1 3835 5th Ave 1 4434 Lincoln Ave 1 3654 Cherokee Ave 1 9169 Cherokee Hwy 1 6191 Oak Lane 1 3282 4th Lane 1 4058 Tree Drive 1 4652 Flute Drive 1 2201 4th Lane 1 8991 Embaracadero Ave 1 1687 3rd Lane 1 5022 1st St 1 5855 Apache St 1 3872 5th Drive 1 7819 Oak St 1 7426 Rock Drive 1 7112 Weaver Ave 1 2117 Lincoln Hwy 1 1135 Solo Lane 1 5053 Tree Drive 1 5506 Best St 1 2696 Cherokee Ridge 1 6770 1st St 1 9794 Embaracadero St 1 4905 Francis Ave 1 9879 Apache Drive 1 5483 Francis Drive 1 2048 3rd Ridge 1 2733 Texas Drive 1 8233 Tree Drive 1 2215 Best Ave 1 2942 1st Lane 1 4965 MLK Drive 1 5124 Maple St 1 1598 3rd Drive 1 5667 4th Drive 1 7785 Lincoln Lane 1 2352 MLK Drive 1 3706 Texas Hwy 1 8917 Cherokee Lane 1 Name: incident_location, dtype: int64 FRAUD_REPORTED : 2 Y 247 N 753 Name: fraud_reported, dtype: int64
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
fraud_con['fraud_reported']=le.fit_transform(fraud_con['fraud_reported'])
fraud_con.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 38 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null int8 12 insured_hobbies 1000 non-null int8 13 insured_relationship 1000 non-null int8 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null int8 18 collision_type 1000 non-null int8 19 incident_severity 1000 non-null int8 20 authorities_contacted 1000 non-null int8 21 incident_state 1000 non-null int8 22 incident_city 1000 non-null int8 23 incident_location 1000 non-null object 24 incident_hour_of_the_day 1000 non-null int64 25 number_of_vehicles_involved 1000 non-null int64 26 property_damage 1000 non-null int8 27 bodily_injuries 1000 non-null int64 28 witnesses 1000 non-null int64 29 police_report_available 1000 non-null int8 30 total_claim_amount 999 non-null float64 31 injury_claim 1000 non-null int64 32 property_claim 994 non-null float64 33 vehicle_claim 1000 non-null int64 34 auto_make 1000 non-null int8 35 auto_model 1000 non-null int8 36 auto_year 1000 non-null int64 37 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(16), object(3) memory usage: 227.5+ KB
fraud_enc = fraud_con.copy()
fraud_enc=fraud_enc.drop('incident_location',axis=1)
fraud_enc.shape
(1000, 37)
fraud_enc.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_bind_date 1000 non-null object 3 policy_state 1000 non-null int8 4 policy_csl 1000 non-null int8 5 policy_deductable 1000 non-null int64 6 policy_annual_premium 991 non-null float64 7 umbrella_limit 799 non-null float64 8 insured_zip 1000 non-null int64 9 insured_sex 1000 non-null int32 10 insured_education_level 1000 non-null int8 11 insured_occupation 1000 non-null int8 12 insured_hobbies 1000 non-null int8 13 insured_relationship 1000 non-null int8 14 capital-gains 1000 non-null int64 15 capital-loss 1000 non-null int64 16 incident_date 1000 non-null object 17 incident_type 1000 non-null int8 18 collision_type 1000 non-null int8 19 incident_severity 1000 non-null int8 20 authorities_contacted 1000 non-null int8 21 incident_state 1000 non-null int8 22 incident_city 1000 non-null int8 23 incident_hour_of_the_day 1000 non-null int64 24 number_of_vehicles_involved 1000 non-null int64 25 property_damage 1000 non-null int8 26 bodily_injuries 1000 non-null int64 27 witnesses 1000 non-null int64 28 police_report_available 1000 non-null int8 29 total_claim_amount 999 non-null float64 30 injury_claim 1000 non-null int64 31 property_claim 994 non-null float64 32 vehicle_claim 1000 non-null int64 33 auto_make 1000 non-null int8 34 auto_model 1000 non-null int8 35 auto_year 1000 non-null int64 36 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(16), object(2) memory usage: 219.7+ KB
final_df = fraud_enc.drop(['policy_bind_date', 'incident_date','auto_model'], axis = 1)
final_df.columns
Index(['months_as_customer', 'age', 'policy_state', 'policy_csl',
'policy_deductable', 'policy_annual_premium', 'umbrella_limit',
'insured_zip', 'insured_sex', 'insured_education_level',
'insured_occupation', 'insured_hobbies', 'insured_relationship',
'capital-gains', 'capital-loss', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted', 'incident_state',
'incident_city', 'incident_hour_of_the_day',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'witnesses', 'police_report_available', 'total_claim_amount',
'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
'auto_year', 'fraud_reported'],
dtype='object')
final_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1000 entries, 521585 to 556080 Data columns (total 34 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null int64 1 age 996 non-null float64 2 policy_state 1000 non-null int8 3 policy_csl 1000 non-null int8 4 policy_deductable 1000 non-null int64 5 policy_annual_premium 991 non-null float64 6 umbrella_limit 799 non-null float64 7 insured_zip 1000 non-null int64 8 insured_sex 1000 non-null int32 9 insured_education_level 1000 non-null int8 10 insured_occupation 1000 non-null int8 11 insured_hobbies 1000 non-null int8 12 insured_relationship 1000 non-null int8 13 capital-gains 1000 non-null int64 14 capital-loss 1000 non-null int64 15 incident_type 1000 non-null int8 16 collision_type 1000 non-null int8 17 incident_severity 1000 non-null int8 18 authorities_contacted 1000 non-null int8 19 incident_state 1000 non-null int8 20 incident_city 1000 non-null int8 21 incident_hour_of_the_day 1000 non-null int64 22 number_of_vehicles_involved 1000 non-null int64 23 property_damage 1000 non-null int8 24 bodily_injuries 1000 non-null int64 25 witnesses 1000 non-null int64 26 police_report_available 1000 non-null int8 27 total_claim_amount 999 non-null float64 28 injury_claim 1000 non-null int64 29 property_claim 994 non-null float64 30 vehicle_claim 1000 non-null int64 31 auto_make 1000 non-null int8 32 auto_year 1000 non-null int64 33 fraud_reported 1000 non-null int32 dtypes: float64(5), int32(2), int64(12), int8(15) memory usage: 203.1 KB
from sklearn.impute import KNNImputer
imp = KNNImputer(n_neighbors=10)
fraud_imp = pd.DataFrame(imp.fit_transform(final_df),columns=final_df.columns)
fraud_imp.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 34 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 months_as_customer 1000 non-null float64 1 age 1000 non-null float64 2 policy_state 1000 non-null float64 3 policy_csl 1000 non-null float64 4 policy_deductable 1000 non-null float64 5 policy_annual_premium 1000 non-null float64 6 umbrella_limit 1000 non-null float64 7 insured_zip 1000 non-null float64 8 insured_sex 1000 non-null float64 9 insured_education_level 1000 non-null float64 10 insured_occupation 1000 non-null float64 11 insured_hobbies 1000 non-null float64 12 insured_relationship 1000 non-null float64 13 capital-gains 1000 non-null float64 14 capital-loss 1000 non-null float64 15 incident_type 1000 non-null float64 16 collision_type 1000 non-null float64 17 incident_severity 1000 non-null float64 18 authorities_contacted 1000 non-null float64 19 incident_state 1000 non-null float64 20 incident_city 1000 non-null float64 21 incident_hour_of_the_day 1000 non-null float64 22 number_of_vehicles_involved 1000 non-null float64 23 property_damage 1000 non-null float64 24 bodily_injuries 1000 non-null float64 25 witnesses 1000 non-null float64 26 police_report_available 1000 non-null float64 27 total_claim_amount 1000 non-null float64 28 injury_claim 1000 non-null float64 29 property_claim 1000 non-null float64 30 vehicle_claim 1000 non-null float64 31 auto_make 1000 non-null float64 32 auto_year 1000 non-null float64 33 fraud_reported 1000 non-null float64 dtypes: float64(34) memory usage: 265.8 KB
X = fraud_imp.drop('fraud_reported',axis=1)
Y = fraud_imp['fraud_reported']
Y.value_counts()
0.0 753 1.0 247 Name: fraud_reported, dtype: int64
X.shape
(1000, 33)
X.drop(['policy_state', 'policy_deductable', 'umbrella_limit',
'insured_zip', 'insured_hobbies','capital-gains', 'capital-loss', 'incident_state',
'incident_city', 'incident_hour_of_the_day',
'witnesses','auto_year'],axis=1,inplace=True)
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-321-9ad6a6e7830f> in <module> ----> 1 X.drop(['policy_state', 'policy_deductable', 'umbrella_limit', 2 'insured_zip', 'insured_hobbies','capital-gains', 'capital-loss', 'incident_state', 3 'incident_city', 'incident_hour_of_the_day', 4 'witnesses','auto_year'],axis=1,inplace=True) ~\Anaconda3\lib\site-packages\pandas\core\frame.py in drop(self, labels, axis, index, columns, level, inplace, errors) 4161 weight 1.0 0.8 4162 """ -> 4163 return super().drop( 4164 labels=labels, 4165 axis=axis, ~\Anaconda3\lib\site-packages\pandas\core\generic.py in drop(self, labels, axis, index, columns, level, inplace, errors) 3885 for axis, labels in axes.items(): 3886 if labels is not None: -> 3887 obj = obj._drop_axis(labels, axis, level=level, errors=errors) 3888 3889 if inplace: ~\Anaconda3\lib\site-packages\pandas\core\generic.py in _drop_axis(self, labels, axis, level, errors) 3919 new_axis = axis.drop(labels, level=level, errors=errors) 3920 else: -> 3921 new_axis = axis.drop(labels, errors=errors) 3922 result = self.reindex(**{axis_name: new_axis}) 3923 ~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in drop(self, labels, errors) 5280 if mask.any(): 5281 if errors != "ignore": -> 5282 raise KeyError(f"{labels[mask]} not found in axis") 5283 indexer = indexer[~mask] 5284 return self.delete(indexer) KeyError: "['policy_state' 'policy_deductable' 'umbrella_limit' 'insured_zip'\n 'insured_hobbies' 'capital-gains' 'capital-loss' 'incident_state'\n 'incident_city' 'incident_hour_of_the_day' 'witnesses' 'auto_year'] not found in axis"
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
scaled_pred = pd.DataFrame(sc.fit_transform(X),columns=X.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(scaled_pred, Y, test_size = 0.2, random_state = 0)
x_train.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_train.shape
(800, 21)
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train,y_train)
pred_model = model_1.predict(x_test)
print("Training Accuracy: ", model_1.score(x_train, y_train))
print('Testing Accuarcy: ', model_1.score(x_test, y_test))
Training Accuracy: 0.7975 Testing Accuarcy: 0.75
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test, pred_model)
print(cr)
precision recall f1-score support
0.0 0.77 0.92 0.84 143
1.0 0.62 0.32 0.42 57
accuracy 0.75 200
macro avg 0.70 0.62 0.63 200
weighted avg 0.73 0.75 0.72 200
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train,y_train)
rf_pred_model = rf_model.predict(x_test)
print("Training Accuracy: ", rf_model.score(x_train, y_train))
print('Testing Accuarcy: ', rf_model.score(x_test, y_test))
Training Accuracy: 1.0 Testing Accuarcy: 0.76
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.79 0.91 0.84 143
1.0 0.63 0.39 0.48 57
accuracy 0.76 200
macro avg 0.71 0.65 0.66 200
weighted avg 0.74 0.76 0.74 200
from imblearn.over_sampling import SMOTE
oversample = SMOTE()
X_res, y_res = oversample.fit_resample(X, Y)
from sklearn.model_selection import train_test_split
x_train_smote, x_test_smote, y_train_smote, y_test_smote = train_test_split(X_res, y_res, test_size = 0.2, random_state = 0)
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train_smote,y_train_smote)
pred_model = model_1.predict(x_test_smote)
print("Training Accuracy: ", model_1.score(x_train_smote, y_train_smote))
print('Testing Accuarcy: ', model_1.score(x_test_smote, y_test_smote))
Training Accuracy: 0.6270764119601329 Testing Accuarcy: 0.5960264900662252
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote, pred_model)
print(cr)
precision recall f1-score support
0.0 0.61 0.58 0.59 154
1.0 0.58 0.61 0.60 148
accuracy 0.60 302
macro avg 0.60 0.60 0.60 302
weighted avg 0.60 0.60 0.60 302
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train_smote,y_train_smote)
rf_pred_model = rf_model.predict(x_test_smote)
print("Training Accuracy: ", rf_model.score(x_train_smote, y_train_smote))
print('Testing Accuarcy: ', rf_model.score(x_test_smote, y_test_smote))
Training Accuracy: 1.0 Testing Accuarcy: 0.8443708609271523
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.85 0.85 0.85 154
1.0 0.84 0.84 0.84 148
accuracy 0.84 302
macro avg 0.84 0.84 0.84 302
weighted avg 0.84 0.84 0.84 302
import pickle
filename = 'final_model.pkl'
pickle.dump(rf_model, open(filename, 'wb'))
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(x_test, y_test)
print(result)
0.435
fraud_con.columns
Index(['months_as_customer', 'age', 'policy_bind_date', 'policy_state',
'policy_csl', 'policy_deductable', 'policy_annual_premium',
'umbrella_limit', 'insured_zip', 'insured_sex',
'insured_education_level', 'insured_occupation', 'insured_hobbies',
'insured_relationship', 'capital-gains', 'capital-loss',
'incident_date', 'incident_type', 'collision_type', 'incident_severity',
'authorities_contacted', 'incident_state', 'incident_city',
'incident_location', 'incident_hour_of_the_day',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'witnesses', 'police_report_available', 'total_claim_amount',
'injury_claim', 'property_claim', 'vehicle_claim', 'auto_make',
'auto_model', 'auto_year', 'fraud_reported'],
dtype='object')
X_res.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_res=X_res.copy()
# 'capital-gains', 'capital-loss', 'incident_type','number_of_vehicles_involved','bodily_injuries',
# 'witnesses'],axis=1)
# 'policy_deductable', 'policy_annual_premium', 'umbrella_limit',
# 'insured_zip', 'insured_hobbies',
# 'capital-gains', 'capital-loss', 'incident_state',
# 'incident_city', 'incident_hour_of_the_day',
# 'number_of_vehicles_involved', 'bodily_injuries',
# 'witnesses', 'total_claim_amount',
# 'injury_claim'],axis=1,inplace=True)
x_res.drop(['vehicle_claim','auto_year'],axis=1,inplace=True)
x_res.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
x_res.shape
(1506, 21)
from sklearn.feature_selection import RFE
rfe = RFE(LR, 20)
rfe = rfe.fit(X_res, y_res.values.ravel())
print(rfe.support_)
print(rfe.ranking_)
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass n_features_to_select=20 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
warnings.warn(f"Pass {args_msg} as keyword args. From version "
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
[ True False False False True True False False False False False True True False False True True True True True True True False True False True False True True True True True True] [ 1 6 12 8 1 1 14 11 2 13 7 1 1 9 10 1 1 1 1 1 1 1 3 1 4 1 5 1 1 1 1 1 1]
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
from sklearn.model_selection import train_test_split
x_train_smote_upd, x_test_smote_upd, y_train_smote_upd, y_test_smote_upd = train_test_split(x_res, y_res, test_size = 0.2, random_state = 0)
from xgboost import XGBClassifier
model=XGBClassifier(use_label_encoder=False)
xgbmodel_clf = model.fit(x_train_smote_upd,y_train_smote_upd)
print(xgbmodel_clf)
[12:49:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.300000012, max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
tree_method='exact', use_label_encoder=False,
validate_parameters=1, verbosity=None)
pred_xbgclf_model = model.predict(x_test_smote_upd)
print("Training Accuracy: ", xgbmodel_clf.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', xgbmodel_clf.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 1.0 Testing Accuarcy: 0.8344370860927153
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, pred_xbgclf_model)
print(cr)
precision recall f1-score support
0.0 0.83 0.85 0.84 154
1.0 0.84 0.82 0.83 148
accuracy 0.83 302
macro avg 0.83 0.83 0.83 302
weighted avg 0.83 0.83 0.83 302
import pickle
filename = 'final_model.pkl'
pickle.dump(xgbmodel_clf, open(filename, 'wb'))
loaded_model = pickle.load(open(filename, 'rb'))
result = loaded_model.score(x_test_smote_upd, y_test_smote_upd)
print(result)
0.8344370860927153
from sklearn.linear_model import LogisticRegression
LR = LogisticRegression()
model_1 = LR.fit(x_train_smote_upd,y_train_smote_upd)
pred_model = model_1.predict(x_test_smote_upd)
print("Training Accuracy: ", model_1.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', model_1.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 0.7641196013289037 Testing Accuarcy: 0.6821192052980133
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, pred_model)
print(cr)
precision recall f1-score support
0.0 0.69 0.68 0.68 154
1.0 0.67 0.69 0.68 148
accuracy 0.68 302
macro avg 0.68 0.68 0.68 302
weighted avg 0.68 0.68 0.68 302
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(x_train_smote_upd,y_train_smote_upd)
rf_pred_model = rf_model.predict(x_test_smote_upd)
print("Training Accuracy: ", rf_model.score(x_train_smote_upd, y_train_smote_upd))
print('Testing Accuarcy: ', rf_model.score(x_test_smote_upd, y_test_smote_upd))
Training Accuracy: 1.0 Testing Accuarcy: 0.847682119205298
from sklearn.metrics import confusion_matrix,classification_report
cr = classification_report(y_test_smote_upd, rf_pred_model)
print(cr)
precision recall f1-score support
0.0 0.85 0.85 0.85 154
1.0 0.84 0.84 0.84 148
accuracy 0.85 302
macro avg 0.85 0.85 0.85 302
weighted avg 0.85 0.85 0.85 302
fraud_con.police_report_available.value_counts()
1 343 0 343 2 314 Name: police_report_available, dtype: int64
import gradio as gr
def encode_age(df): # Binning ages
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
categories = pd.cut(df.Age, bins, labels=False)
df.Age = categories
return df
def encode_education(df):
mapping = {"JD":3,"High School":2,"Associate":0,"MD":4,"Masters":5,"PhD":6,"College":1}
return df.replace({'Education': mapping})
def encode_incident_type(df):
mapping = {"Multi-vehicle Collision":0,"Single Vehicle Collision":2,"Vehicle Theft":3,"Parked Car":1}
return df.replace({'Incident Type': mapping})
def encode_relationship(df):
mapping = {"own-child":3,"other-relative":2,"not-in-family":1,"husband":0,"wife":5,"unmarried":4}
return df.replace({'Relationship': mapping})
def encode_occupation(df):
mapping = {"machine-op-inspct":6,"prof-specialty":9,"tech-support":12,"sales":11,"exec-managerial":3,"craft-repair":2,"transport-moving":13,"other-service":8,"priv-house-serv":7,"armed-forces":1,"adm-clerical":0,"protective-serv":10,"handlers-cleaners":5,"farming-fishing":4}
return df.replace({'Occupation': mapping})
def encode_state(df):
mapping = {"OH":2,"IL":0,"IN": 1}
return df.replace({'State of Policy': mapping})
def encode_coll_type(df):
mapping = {"Rear Collision":1,"Side Collision":2,"Front Collision":0,"Unknown":3}
return df.replace({'Collision Type': mapping})
def encode_incident_severity(df):
mapping = {"Minor Damage":1,"Total Loss":2,"Major Damage":0,"Trivial Damage":3}
return df.replace({'Severity': mapping})
def encode_policy_csl(df):
mapping = {"250/500":1,"100/300":0,"500/1000":2}
return df.replace({'Policy CSL': mapping})
def encode_property_damage(df):
mapping = {"Unknown":1,"NO":0,"YES":2}
return df.replace({'Property Damage': mapping})
def encode_police_rep(df):
mapping = {"Unknown":1,"NO":0,"YES":2}
return df.replace({'Police_Report_Available': mapping})
def encode_auth_con(df):
mapping = {"Police":4,"Fire":1,"Other":3,"Ambulance":0,"None":2}
return df.replace({'Authorities': mapping})
def encode_sex(df):
mapping = {"male": 0, "female": 1}
return df.replace({'Gender': mapping})
def encode_auto_make(df):
mapping = {"Dodge":11,"Suburu":10,"Saab":4,"Nissan":9,"Chevrolet":3,"Ford":5,"BMW":2,"Toyota":12,"Audi":1,"Accura":13,"Volkswagen":0,"Jeep":7,"Mercedes":8,"Honda":6}
return df.replace({'Auto Make': mapping})
from sklearn.preprocessing import StandardScaler
st = StandardScaler()
X_res.columns
Index(['months_as_customer', 'age', 'policy_csl', 'policy_annual_premium',
'insured_sex', 'insured_education_level', 'insured_occupation',
'insured_relationship', 'incident_type', 'collision_type',
'incident_severity', 'authorities_contacted',
'number_of_vehicles_involved', 'property_damage', 'bodily_injuries',
'police_report_available', 'total_claim_amount', 'injury_claim',
'property_claim', 'vehicle_claim', 'auto_make'],
dtype='object')
def predict_fraud(months_as_customer,age,sex, insured_education_level,insured_occupation, insured_relationship,incident_type, collision_type,incident_severity,number_of_vehicles_involved, authorities_contacted, bodily_injuries,property_damage, police_report_available, auto_make,policy_csl,policy_annual_premium,total_claim_amount,injury_claim,property_claim,vehicle_claim):
df = pd.DataFrame.from_dict({'Months':[months_as_customer],'Age':[age],'Gender':[sex],'Education':[insured_education_level],'Occupation':[insured_occupation],'Relationship':[insured_relationship],'Incident Type':[incident_type], 'Collision Type':[collision_type],'Severity':[incident_severity],'Vehicles Involved':[number_of_vehicles_involved], 'Authorities':[authorities_contacted],'Injuries':[bodily_injuries], 'Property Damage':[property_damage], 'Police_Report_Available':[police_report_available],'Auto Make':[auto_make],'Policy CSL':[policy_csl],'Annual Premium':[policy_annual_premium],'Total Claim':[total_claim_amount],'Injury Claim Amount':[injury_claim],'Property Claim Amount':[property_claim],'Vehicle Claim':[vehicle_claim]})
df = encode_sex(df)
df = encode_education(df)
df = encode_auto_make(df)
df = encode_relationship(df)
df = encode_age(df)
df = encode_policy_csl(df)
df = encode_occupation(df)
df = encode_incident_type(df)
df = encode_incident_severity(df)
df = encode_coll_type(df)
df = encode_property_damage(df)
df = encode_police_rep(df)
df = encode_auth_con(df)
df = sc.transform(df)
pred = loaded_model.predict_proba(df)[0]
res = {'Not Fraud': float(pred[0]), 'Fraud': float(pred[1])}
return res
months_as_customer = gr.inputs.Number(label="Months")
age = gr.inputs.Slider(minimum=0, maximum=120, default=22, label="Age")
policy_deductable = gr.inputs.Number(label="Deduction")
policy_annual_premium = gr.inputs.Number(label="Annual Premium")
#umbrella_limit = gr.inputs.Number(label="Limit")
insured_education_level = gr.inputs.Dropdown(['JD','High School','Associate','MD','Masters','PhD','College'],label="Education")
insured_occupation = gr.inputs.Dropdown(['machine-op-inspct','prof-specialty','tech-support','sales','exec-managerial','craft-repair','transport-moving','other-service','priv-house-serv','armed-forces','adm-clerical','protective-serv','handlers-cleaners','farming-fishing'],label="Occupation")
auto_make = gr.inputs.Dropdown(['Dodge','Suburu','Saab','Nissan','Chevrolet','Ford','BMW','Toyota','Audi','Accura','Volkswagen','Jeep','Mercedes','Honda'],label="Auto Make")
sex = gr.inputs.Radio(['female', 'male'], label="Gender")
policy_csl = gr.inputs.Radio(['250/500','100/300','500/1000'],label='Polcy CSL')
#capital-gains = gr.inputs.Number(label="Gain")
#capital-loss = gr.inputs.Number(label="Loss")
incident_type = gr.inputs.Dropdown(['Multi-vehicle Collision','Single Vehicle Collision','Vehicle Theft','Parked Car'],label="Incident Type")
insured_relationship = gr.inputs.Dropdown(['own-child','other-relative','not-in-family','husband','wife','unmarried'],label="Relationship")
incident_severity = gr.inputs.Dropdown(['Minor Damage','Total Loss','Major Damage','Trivial Damage'],label="Severity")
collision_type = gr.inputs.Dropdown(['Rear Collision','Side Collision','Front Collision','Unknown'],label="Collision Type")
number_of_vehicles_involved = gr.inputs.Number(label='Vehicles Involved')
property_damage = gr.inputs.Dropdown(['Unknown','NO','YES'],label="Property Damage")
police_report_available = gr.inputs.Dropdown(['Unknown','NO','YES'],label="Police_Report_Available")
authorities_contacted = gr.inputs.Dropdown(['Police','Fire','Other','Ambulance','None'],label="Authorities")
total_claim_amount = gr.inputs.Number(label='Total Claim')
#witnesses = gr.inputs.Number(label='Witnesses')
bodily_injuries = gr.inputs.Number(label='Injuries')
injury_claim = gr.inputs.Number(label='Injury Claim Amount')
property_claim = gr.inputs.Number(label='Property Claim Amount')
vehicle_claim = gr.inputs.Number(label='Vehicle Claim')
gr.Interface(predict_fraud, [months_as_customer,age,sex, insured_education_level,insured_occupation, insured_relationship,incident_type, collision_type,incident_severity,number_of_vehicles_involved, authorities_contacted, bodily_injuries,property_damage, police_report_available, auto_make,policy_csl,policy_annual_premium,total_claim_amount,injury_claim,property_claim,vehicle_claim], "label").launch(share=True)
Running locally at: http://127.0.0.1:7865/ This share link will expire in 72 hours. If you need a permanent link, visit: https://gradio.app/introducing-hosted Running on External URL: https://31334.gradio.app Interface loading below...
(<Flask 'gradio.networking'>, 'http://127.0.0.1:7865/', 'https://31334.gradio.app')
from interpret.glassbox import ExplainableBoostingClassifier
ebm = ExplainableBoostingClassifier()
ebm.fit(x_train_smote_upd,y_train_smote_upd)
ExplainableBoostingClassifier(feature_names=['months_as_customer', 'age',
'policy_csl',
'policy_annual_premium',
'insured_sex',
'insured_education_level',
'insured_occupation',
'insured_relationship',
'incident_type', 'collision_type',
'incident_severity',
'authorities_contacted',
'number_of_vehicles_involved',
'property_damage',
'bodily_injuries',
'police_report...
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'continuous',
'continuous', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction',
'interaction', 'interaction', ...])
from interpret import show
ebm_global = ebm.explain_global()
show(ebm_global)
C:\Users\TSK\Anaconda3\lib\site-packages\interpret\visual\udash.py:5: UserWarning: The dash_html_components package is deprecated. Please replace `import dash_html_components as html` with `from dash import html` import dash_html_components as html C:\Users\TSK\Anaconda3\lib\site-packages\interpret\visual\udash.py:6: UserWarning: The dash_core_components package is deprecated. Please replace `import dash_core_components as dcc` with `from dash import dcc` import dash_core_components as dcc C:\Users\TSK\Anaconda3\lib\site-packages\interpret\visual\udash.py:7: UserWarning: The dash_table package is deprecated. Please replace `import dash_table` with `from dash import dash_table` Also, if you're using any of the table format helpers (e.g. Group), replace `from dash_table.Format import Group` with `from dash.dash_table.Format import Group` import dash_table as dt
<iframe src="http://127.0.0.1:7001/1894287856592/" width=100% height=800 frameBorder="0"></iframe>
Socket exception: An existing connection was forcibly closed by the remote host (10054)
ebm_local = ebm.explain_local(x_test_smote_upd, y_test_smote_upd)
show(ebm_local)
<iframe src="http://127.0.0.1:7001/1894441549936/" width=100% height=800 frameBorder="0"></iframe>
from flaml import AutoML
automl = AutoML()
automl.fit(x_train_smote_upd,y_train_smote_upd, task="classification")
[flaml.automl: 09-27 15:51:39] {1431} INFO - Evaluation method: cv
[flaml.automl: 09-27 15:51:39] {1477} INFO - Minimizing error metric: 1-roc_auc
[flaml.automl: 09-27 15:51:40] {1514} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 0, current learner lgbm
[flaml.automl: 09-27 15:51:40] {1925} INFO - at 1.4s, best lgbm's error=0.1582, best lgbm's error=0.1582
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 1, current learner lgbm
[flaml.automl: 09-27 15:51:40] {1925} INFO - at 1.5s, best lgbm's error=0.1357, best lgbm's error=0.1357
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 2, current learner lgbm
[flaml.automl: 09-27 15:51:40] {1925} INFO - at 1.6s, best lgbm's error=0.1357, best lgbm's error=0.1357
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 3, current learner lgbm
[flaml.automl: 09-27 15:51:40] {1925} INFO - at 1.6s, best lgbm's error=0.1076, best lgbm's error=0.1076
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 4, current learner xgboost
[flaml.automl: 09-27 15:51:40] {1925} INFO - at 1.7s, best xgboost's error=0.1545, best lgbm's error=0.1076
[flaml.automl: 09-27 15:51:40] {1746} INFO - iteration 5, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 1.8s, best lgbm's error=0.1076, best lgbm's error=0.1076
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 6, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 1.8s, best lgbm's error=0.0864, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 7, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 1.9s, best lgbm's error=0.0864, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 8, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.0s, best lgbm's error=0.0864, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 9, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.0s, best lgbm's error=0.0864, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 10, current learner xgboost
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.1s, best xgboost's error=0.1436, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 11, current learner extra_tree
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.2s, best extra_tree's error=0.1714, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 12, current learner rf
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.3s, best rf's error=0.1575, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 13, current learner rf
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.4s, best rf's error=0.1208, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 14, current learner rf
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.5s, best rf's error=0.1208, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 15, current learner extra_tree
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.6s, best extra_tree's error=0.1359, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 16, current learner lgbm
[flaml.automl: 09-27 15:51:41] {1925} INFO - at 2.7s, best lgbm's error=0.0864, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:41] {1746} INFO - iteration 17, current learner extra_tree
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 2.8s, best extra_tree's error=0.1359, best lgbm's error=0.0864
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 18, current learner lgbm
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 2.9s, best lgbm's error=0.0719, best lgbm's error=0.0719
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 19, current learner xgboost
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.0s, best xgboost's error=0.1436, best lgbm's error=0.0719
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 20, current learner lgbm
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.1s, best lgbm's error=0.0650, best lgbm's error=0.0650
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 21, current learner lgbm
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.2s, best lgbm's error=0.0650, best lgbm's error=0.0650
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 22, current learner lgbm
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.3s, best lgbm's error=0.0650, best lgbm's error=0.0650
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 23, current learner lgbm
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.4s, best lgbm's error=0.0647, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 24, current learner xgboost
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.5s, best xgboost's error=0.1431, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 25, current learner extra_tree
[flaml.automl: 09-27 15:51:42] {1925} INFO - at 3.6s, best extra_tree's error=0.1359, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:42] {1746} INFO - iteration 26, current learner lgbm
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 3.8s, best lgbm's error=0.0647, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 27, current learner rf
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 3.9s, best rf's error=0.1208, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 28, current learner extra_tree
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.0s, best extra_tree's error=0.1359, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 29, current learner extra_tree
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.1s, best extra_tree's error=0.1359, best lgbm's error=0.0647
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 30, current learner lgbm
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.2s, best lgbm's error=0.0645, best lgbm's error=0.0645
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 31, current learner lgbm
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.3s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 32, current learner lgbm
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.4s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 33, current learner rf
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.6s, best rf's error=0.1091, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 34, current learner lgbm
[flaml.automl: 09-27 15:51:43] {1925} INFO - at 4.7s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:43] {1746} INFO - iteration 35, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 4.8s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 36, current learner extra_tree
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.0s, best extra_tree's error=0.1176, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 37, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.1s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 38, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.2s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 39, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.3s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 40, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.4s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 41, current learner rf
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.6s, best rf's error=0.1091, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 42, current learner lgbm
[flaml.automl: 09-27 15:51:44] {1925} INFO - at 5.7s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:44] {1746} INFO - iteration 43, current learner rf
[flaml.automl: 09-27 15:51:45] {1925} INFO - at 5.8s, best rf's error=0.1091, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:45] {1746} INFO - iteration 44, current learner extra_tree
[flaml.automl: 09-27 15:51:45] {1925} INFO - at 5.9s, best extra_tree's error=0.0945, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:45] {1746} INFO - iteration 45, current learner extra_tree
[flaml.automl: 09-27 15:51:45] {1925} INFO - at 6.0s, best extra_tree's error=0.0945, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:45] {1746} INFO - iteration 46, current learner lgbm
[flaml.automl: 09-27 15:51:45] {1925} INFO - at 6.1s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:45] {1746} INFO - iteration 47, current learner extra_tree
[flaml.automl: 09-27 15:51:45] {1925} INFO - at 6.3s, best extra_tree's error=0.0945, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:45] {1746} INFO - iteration 48, current learner catboost
[flaml.automl: 09-27 15:51:47] {1925} INFO - at 7.8s, best catboost's error=0.0655, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:47] {1746} INFO - iteration 49, current learner lgbm
[flaml.automl: 09-27 15:51:47] {1925} INFO - at 7.9s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:47] {1746} INFO - iteration 50, current learner catboost
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 9.8s, best catboost's error=0.0636, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 51, current learner extra_tree
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 9.9s, best extra_tree's error=0.0792, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 52, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.0s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 53, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.1s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 54, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.2s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 55, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.3s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 56, current learner extra_tree
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.4s, best extra_tree's error=0.0792, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 57, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.6s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 58, current learner lgbm
[flaml.automl: 09-27 15:51:49] {1925} INFO - at 10.7s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:49] {1746} INFO - iteration 59, current learner catboost
[flaml.automl: 09-27 15:51:51] {1925} INFO - at 12.1s, best catboost's error=0.0636, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:51] {1746} INFO - iteration 60, current learner lgbm
[flaml.automl: 09-27 15:51:51] {1925} INFO - at 12.1s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:51] {1746} INFO - iteration 61, current learner extra_tree
[flaml.automl: 09-27 15:51:51] {1925} INFO - at 12.3s, best extra_tree's error=0.0714, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:51] {1746} INFO - iteration 62, current learner lgbm
[flaml.automl: 09-27 15:51:51] {1925} INFO - at 12.6s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:51] {1746} INFO - iteration 63, current learner extra_tree
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 12.7s, best extra_tree's error=0.0714, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 64, current learner extra_tree
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 13.1s, best extra_tree's error=0.0714, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 65, current learner lgbm
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 13.2s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 66, current learner lgbm
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 13.3s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 67, current learner extra_tree
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 13.5s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 68, current learner lgbm
[flaml.automl: 09-27 15:51:52] {1925} INFO - at 13.7s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:52] {1746} INFO - iteration 69, current learner lgbm
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 13.9s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 70, current learner rf
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 14.1s, best rf's error=0.1006, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 71, current learner rf
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 14.2s, best rf's error=0.1006, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 72, current learner rf
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 14.3s, best rf's error=0.1006, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 73, current learner extra_tree
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 14.5s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 74, current learner rf
[flaml.automl: 09-27 15:51:53] {1925} INFO - at 14.7s, best rf's error=0.0832, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:53] {1746} INFO - iteration 75, current learner extra_tree
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 14.8s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 76, current learner lgbm
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 14.9s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 77, current learner lgbm
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 15.0s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 78, current learner rf
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 15.2s, best rf's error=0.0832, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 79, current learner rf
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 15.4s, best rf's error=0.0767, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 80, current learner lgbm
[flaml.automl: 09-27 15:51:54] {1925} INFO - at 15.6s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:54] {1746} INFO - iteration 81, current learner extra_tree
[flaml.automl: 09-27 15:51:55] {1925} INFO - at 15.8s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:55] {1746} INFO - iteration 82, current learner lgbm
[flaml.automl: 09-27 15:51:55] {1925} INFO - at 16.0s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:55] {1746} INFO - iteration 83, current learner rf
[flaml.automl: 09-27 15:51:55] {1925} INFO - at 16.2s, best rf's error=0.0767, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:55] {1746} INFO - iteration 84, current learner rf
[flaml.automl: 09-27 15:51:55] {1925} INFO - at 16.6s, best rf's error=0.0767, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:55] {1746} INFO - iteration 85, current learner extra_tree
[flaml.automl: 09-27 15:51:55] {1925} INFO - at 16.7s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:55] {1746} INFO - iteration 86, current learner rf
[flaml.automl: 09-27 15:51:56] {1925} INFO - at 17.0s, best rf's error=0.0767, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:56] {1746} INFO - iteration 87, current learner catboost
[flaml.automl: 09-27 15:51:58] {1925} INFO - at 19.5s, best catboost's error=0.0636, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:58] {1746} INFO - iteration 88, current learner lgbm
[flaml.automl: 09-27 15:51:58] {1925} INFO - at 19.6s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:58] {1746} INFO - iteration 89, current learner lgbm
[flaml.automl: 09-27 15:51:58] {1925} INFO - at 19.7s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:58] {1746} INFO - iteration 90, current learner extra_tree
[flaml.automl: 09-27 15:51:59] {1925} INFO - at 19.9s, best extra_tree's error=0.0684, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:59] {1746} INFO - iteration 91, current learner lgbm
[flaml.automl: 09-27 15:51:59] {1925} INFO - at 20.1s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:59] {1746} INFO - iteration 92, current learner extra_tree
[flaml.automl: 09-27 15:51:59] {1925} INFO - at 20.3s, best extra_tree's error=0.0658, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:59] {1746} INFO - iteration 93, current learner rf
[flaml.automl: 09-27 15:51:59] {1925} INFO - at 20.5s, best rf's error=0.0767, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:59] {1746} INFO - iteration 94, current learner rf
[flaml.automl: 09-27 15:51:59] {1925} INFO - at 20.7s, best rf's error=0.0744, best lgbm's error=0.0575
[flaml.automl: 09-27 15:51:59] {1746} INFO - iteration 95, current learner lgbm
[flaml.automl: 09-27 15:52:00] {1925} INFO - at 20.8s, best lgbm's error=0.0575, best lgbm's error=0.0575
[flaml.automl: 09-27 15:52:00] {1746} INFO - iteration 96, current learner extra_tree
[flaml.automl: 09-27 15:52:00] {1925} INFO - at 21.0s, best extra_tree's error=0.0654, best lgbm's error=0.0575
[flaml.automl: 09-27 15:52:00] {1746} INFO - iteration 97, current learner lgbm
[flaml.automl: 09-27 15:52:00] {1925} INFO - at 21.3s, best lgbm's error=0.0561, best lgbm's error=0.0561
[flaml.automl: 09-27 15:52:00] {1746} INFO - iteration 98, current learner rf
[flaml.automl: 09-27 15:52:00] {1925} INFO - at 21.5s, best rf's error=0.0744, best lgbm's error=0.0561
[flaml.automl: 09-27 15:52:00] {1746} INFO - iteration 99, current learner lgbm
[flaml.automl: 09-27 15:52:00] {1925} INFO - at 21.6s, best lgbm's error=0.0561, best lgbm's error=0.0561
[flaml.automl: 09-27 15:52:00] {1746} INFO - iteration 100, current learner lgbm
[flaml.automl: 09-27 15:52:01] {1925} INFO - at 22.6s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:01] {1746} INFO - iteration 101, current learner xgboost
[flaml.automl: 09-27 15:52:01] {1925} INFO - at 22.7s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:01] {1746} INFO - iteration 102, current learner xgboost
[flaml.automl: 09-27 15:52:02] {1925} INFO - at 22.8s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:02] {1746} INFO - iteration 103, current learner lgbm
[flaml.automl: 09-27 15:52:05] {1925} INFO - at 26.3s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:05] {1746} INFO - iteration 104, current learner xgboost
[flaml.automl: 09-27 15:52:05] {1925} INFO - at 26.5s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:05] {1746} INFO - iteration 105, current learner rf
[flaml.automl: 09-27 15:52:05] {1925} INFO - at 26.7s, best rf's error=0.0744, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:05] {1746} INFO - iteration 106, current learner xgboost
[flaml.automl: 09-27 15:52:06] {1925} INFO - at 26.8s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:06] {1746} INFO - iteration 107, current learner extra_tree
[flaml.automl: 09-27 15:52:06] {1925} INFO - at 27.1s, best extra_tree's error=0.0654, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:06] {1746} INFO - iteration 108, current learner lgbm
[flaml.automl: 09-27 15:52:06] {1925} INFO - at 27.4s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:06] {1746} INFO - iteration 109, current learner xgboost
[flaml.automl: 09-27 15:52:06] {1925} INFO - at 27.5s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:06] {1746} INFO - iteration 110, current learner xgboost
[flaml.automl: 09-27 15:52:06] {1925} INFO - at 27.6s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:06] {1746} INFO - iteration 111, current learner lgbm
[flaml.automl: 09-27 15:52:09] {1925} INFO - at 30.4s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:09] {1746} INFO - iteration 112, current learner extra_tree
[flaml.automl: 09-27 15:52:09] {1925} INFO - at 30.6s, best extra_tree's error=0.0654, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:09] {1746} INFO - iteration 113, current learner xgboost
[flaml.automl: 09-27 15:52:09] {1925} INFO - at 30.7s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:09] {1746} INFO - iteration 114, current learner extra_tree
[flaml.automl: 09-27 15:52:10] {1925} INFO - at 30.9s, best extra_tree's error=0.0654, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:10] {1746} INFO - iteration 115, current learner lgbm
[flaml.automl: 09-27 15:52:10] {1925} INFO - at 31.4s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:10] {1746} INFO - iteration 116, current learner lgbm
[flaml.automl: 09-27 15:52:11] {1925} INFO - at 32.6s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:11] {1746} INFO - iteration 117, current learner xgboost
[flaml.automl: 09-27 15:52:11] {1925} INFO - at 32.7s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:11] {1746} INFO - iteration 118, current learner xgboost
[flaml.automl: 09-27 15:52:12] {1925} INFO - at 32.8s, best xgboost's error=0.1223, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:12] {1746} INFO - iteration 119, current learner lgbm
[flaml.automl: 09-27 15:52:12] {1925} INFO - at 33.4s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:12] {1746} INFO - iteration 120, current learner xgboost
[flaml.automl: 09-27 15:52:12] {1925} INFO - at 33.5s, best xgboost's error=0.1146, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:12] {1746} INFO - iteration 121, current learner xgboost
[flaml.automl: 09-27 15:52:12] {1925} INFO - at 33.6s, best xgboost's error=0.1146, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:12] {1746} INFO - iteration 122, current learner extra_tree
[flaml.automl: 09-27 15:52:13] {1925} INFO - at 33.8s, best extra_tree's error=0.0654, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:13] {1746} INFO - iteration 123, current learner xgboost
[flaml.automl: 09-27 15:52:13] {1925} INFO - at 33.9s, best xgboost's error=0.1146, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:13] {1746} INFO - iteration 124, current learner lgbm
[flaml.automl: 09-27 15:52:13] {1925} INFO - at 34.5s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:13] {1746} INFO - iteration 125, current learner lgbm
[flaml.automl: 09-27 15:52:15] {1925} INFO - at 35.9s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:15] {1746} INFO - iteration 126, current learner lrl1
No low-cost partial config given to the search algorithm. For cost-frugal search, consider providing low-cost values for cost-related hps via 'low_cost_partial_config'.
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
[flaml.automl: 09-27 15:52:15] {1925} INFO - at 36.1s, best lrl1's error=0.3899, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:15] {1746} INFO - iteration 127, current learner lgbm
[flaml.automl: 09-27 15:52:15] {1925} INFO - at 36.6s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:15] {1746} INFO - iteration 128, current learner lgbm
[flaml.automl: 09-27 15:52:17] {1925} INFO - at 38.6s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:17] {1746} INFO - iteration 129, current learner lrl1
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
C:\Users\TSK\Anaconda3\lib\site-packages\sklearn\linear_model\_sag.py:328: ConvergenceWarning:
The max_iter was reached which means the coef_ did not converge
[flaml.automl: 09-27 15:52:18] {1925} INFO - at 38.9s, best lrl1's error=0.3899, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:18] {1746} INFO - iteration 130, current learner xgboost
[flaml.automl: 09-27 15:52:18] {1925} INFO - at 39.1s, best xgboost's error=0.0853, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:18] {1746} INFO - iteration 131, current learner xgboost
[flaml.automl: 09-27 15:52:18] {1925} INFO - at 39.2s, best xgboost's error=0.0853, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:18] {1746} INFO - iteration 132, current learner xgboost
[flaml.automl: 09-27 15:52:18] {1925} INFO - at 39.4s, best xgboost's error=0.0849, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:18] {1746} INFO - iteration 133, current learner xgboost
[flaml.automl: 09-27 15:52:18] {1925} INFO - at 39.5s, best xgboost's error=0.0849, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:18] {1746} INFO - iteration 134, current learner lgbm
[flaml.automl: 09-27 15:52:22] {1925} INFO - at 43.5s, best lgbm's error=0.0528, best lgbm's error=0.0528
[flaml.automl: 09-27 15:52:22] {1746} INFO - iteration 135, current learner lgbm
[flaml.automl: 09-27 15:52:23] {1925} INFO - at 44.2s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:23] {1746} INFO - iteration 136, current learner lgbm
[flaml.automl: 09-27 15:52:24] {1925} INFO - at 44.9s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:24] {1746} INFO - iteration 137, current learner extra_tree
[flaml.automl: 09-27 15:52:24] {1925} INFO - at 45.1s, best extra_tree's error=0.0654, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:24] {1746} INFO - iteration 138, current learner lgbm
[flaml.automl: 09-27 15:52:25] {1925} INFO - at 45.7s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:25] {1746} INFO - iteration 139, current learner lgbm
[flaml.automl: 09-27 15:52:25] {1925} INFO - at 46.3s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:25] {1746} INFO - iteration 140, current learner rf
[flaml.automl: 09-27 15:52:25] {1925} INFO - at 46.5s, best rf's error=0.0744, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:25] {1746} INFO - iteration 141, current learner lgbm
[flaml.automl: 09-27 15:52:26] {1925} INFO - at 47.0s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:26] {1746} INFO - iteration 142, current learner rf
[flaml.automl: 09-27 15:52:26] {1925} INFO - at 47.3s, best rf's error=0.0744, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:26] {1746} INFO - iteration 143, current learner lgbm
[flaml.automl: 09-27 15:52:27] {1925} INFO - at 47.9s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:27] {1746} INFO - iteration 144, current learner rf
[flaml.automl: 09-27 15:52:27] {1925} INFO - at 48.1s, best rf's error=0.0744, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:27] {1746} INFO - iteration 145, current learner lgbm
[flaml.automl: 09-27 15:52:28] {1925} INFO - at 48.7s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:28] {1746} INFO - iteration 146, current learner lgbm
[flaml.automl: 09-27 15:52:30] {1925} INFO - at 51.4s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:30] {1746} INFO - iteration 147, current learner lgbm
[flaml.automl: 09-27 15:52:30] {1925} INFO - at 51.7s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:30] {1746} INFO - iteration 148, current learner lgbm
[flaml.automl: 09-27 15:52:31] {1925} INFO - at 52.3s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:31] {1746} INFO - iteration 149, current learner lgbm
[flaml.automl: 09-27 15:52:32] {1925} INFO - at 52.9s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:32] {1746} INFO - iteration 150, current learner lgbm
[flaml.automl: 09-27 15:52:32] {1925} INFO - at 53.2s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:32] {1746} INFO - iteration 151, current learner lgbm
[flaml.automl: 09-27 15:52:34] {1925} INFO - at 55.0s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:34] {1746} INFO - iteration 152, current learner lgbm
[flaml.automl: 09-27 15:52:35] {1925} INFO - at 56.2s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:35] {1746} INFO - iteration 153, current learner rf
[flaml.automl: 09-27 15:52:35] {1925} INFO - at 56.5s, best rf's error=0.0744, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:35] {1746} INFO - iteration 154, current learner lgbm
[flaml.automl: 09-27 15:52:36] {1925} INFO - at 56.9s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:36] {1746} INFO - iteration 155, current learner lgbm
[flaml.automl: 09-27 15:52:36] {1925} INFO - at 57.2s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:36] {1746} INFO - iteration 156, current learner lgbm
[flaml.automl: 09-27 15:52:38] {1925} INFO - at 59.4s, best lgbm's error=0.0507, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:38] {1746} INFO - iteration 157, current learner extra_tree
[flaml.automl: 09-27 15:52:38] {1925} INFO - at 59.5s, best extra_tree's error=0.0654, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:38] {1746} INFO - iteration 158, current learner xgboost
[flaml.automl: 09-27 15:52:39] {1925} INFO - at 60.0s, best xgboost's error=0.0796, best lgbm's error=0.0507
[flaml.automl: 09-27 15:52:39] {2032} INFO - selected model: LGBMClassifier(colsample_bytree=0.4287108903387949,
learning_rate=0.023238500386038714, max_bin=256,
min_child_samples=4, n_estimators=63, num_leaves=237,
objective='binary', reg_alpha=0.0015770070728361961,
reg_lambda=0.0009765625, verbose=-1)
[flaml.automl: 09-27 15:52:39] {2093} INFO - retrain lgbm for 0.2s
[flaml.automl: 09-27 15:52:39] {2099} INFO - retrained model: LGBMClassifier(colsample_bytree=0.4287108903387949,
learning_rate=0.023238500386038714, max_bin=256,
min_child_samples=4, n_estimators=63, num_leaves=237,
objective='binary', reg_alpha=0.0015770070728361961,
reg_lambda=0.0009765625, verbose=-1)
[flaml.automl: 09-27 15:52:39] {1538} INFO - fit succeeded
[flaml.automl: 09-27 15:52:39] {1539} INFO - Time taken to find the best model: 44.18676519393921
[flaml.automl: 09-27 15:52:39] {1550} WARNING - Time taken to find the best model is 74% of the provided time budget and not all estimators' hyperparameter search converged. Consider increasing the time budget.
print('Best ML leaner:', automl.best_estimator)
print('Best hyperparmeter config:', automl.best_config)
print('Best accuracy on validation data: {0:.4g}'.format(1-automl.best_loss))
print('Training duration of best run: {0:.4g} s'.format(automl.best_config_train_time))
Best ML leaner: extra_tree
Best hyperparmeter config: {'n_estimators': 103, 'max_features': 0.17131679426949367, 'max_leaves': 411, 'criterion': 'gini'}
Best accuracy on validation data: 0.9463
Training duration of best run: 0.6583 s
Socket exception: An existing connection was forcibly closed by the remote host (10054)